Non-invasive detection of tissue abnormality using methylation

ABSTRACT

Systems, methods, and apparatuses can determine and use methylation profiles of various tissues and samples. Examples are provided. A methylation profile can be deduced for fetal/tumor tissue based on a comparison of plasma methylation (or other sample with cell-free DNA) to a methylation profile of the mother/patient. A methylation profile can be determined for fetal/tumor tissue using tissue-specific alleles to identify DNA from the fetus/tumor when the sample has a mixture of DNA. A methylation profile can be used to determine copy number variations in genome of a fetus/tumor. Methylation markers for a fetus have been identified via various techniques. The methylation profile can be determined by determining a size parameter of a size distribution of DNA fragments, where reference values for the size parameter can be used to determine methylation levels. Additionally, a methylation level can be used to determine a level of cancer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/903,231 entitled “Cancer Screening UsingMethylation Of CPG Islands,” filed on Jun. 16, 2020, which is acontinuation application of U.S. patent application Ser. No. 16/389,753entitled “Non-Invasive Determination Of Methylome Of Tumor From Plasma,”filed on Apr. 19, 2019 (Now U.S. Pat. No. 10,706,957), which is acontinuation application of U.S. patent application Ser. No. 14/495,791entitled “Non-Invasive Determination Of Methylome Of Tumor From Plasma,”filed on Sep. 24, 2014 (now U.S. Pat. No. 10,392,666), which is acontinuation application of International Patent Application No.PCT/AU2013/001088 entitled “Non-Invasive Determination Of Methylome OfFetus Or Tumor From Plasma,” filed on Sep. 20, 2013, which claimspriority to U.S. Provisional Patent Application No. 61/830,571 entitled“Tumor Detection In Plasma Using Methylation Status And Copy Number,”filed on Jun. 3, 2013, and which is a continuation-in-part applicationof U.S. patent application Ser. No. 13/842,209 entitled “Non-InvasiveDetermination Of Methylome Of Fetus Or Tumor From Plasma,” filed on Mar.15, 2013 (now U.S. Pat. No. 9,732,390), which is a non-provisional ofand claims the benefit of U.S. Provisional Patent Application No.61/703,512 entitled “Method Of Determining The Whole Genome DNAMethylation Status Of The Placenta By Massively Parallel Sequencing OfMaternal Plasma,” filed on Sep. 20, 2012, which are herein incorporatedby reference in their entirety for all purposes.

FIELD

The present disclosure relates generally a determination of amethylation pattern (methylome) of DNA, and more particularly toanalyzing a biological sample (e.g., plasma) that includes a mixture ofDNA from different genomes (e.g., from fetus and mother, or from tumorand normal cells) to determine the methylation pattern (methylome) ofthe minority genome. Uses of the determined methylome are alsodescribed.

BACKGROUND

Embryonic and fetal development is a complex process and involves aseries of highly orchestrated genetic and epigenetic events. Cancerdevelopment is also a complex process involving typically multiplegenetic and epigenetic steps. Abnormalities in the epigenetic control ofdevelopmental processes are implicated in infertility, spontaneousabortion, intrauterine growth abnormalities and postnatal consequences.DNA methylation is one of the most frequently studied epigeneticmechanisms. Methylation of DNA mostly occurs in the context of theaddition of a methyl group to the 5′ carbon of cytosine residues amongCpG dinucleotides. Cytosine methylation adds a layer of control to genetranscription and DNA function. For example, hypermethylation of genepromoters enriched with CpG dinucleotides, termed CpG islands, istypically associated with repression of gene function.

Despite the important role of epigenetic mechanisms in mediatingdevelopmental processes, human embryonic and fetal tissues are notreadily accessible for analysis (tumors may similarly not beaccessible). Studies of the dynamic changes of such epigenetic processesin health and disease during the prenatal period in humans are virtuallyimpossible. Extraembryonic tissues, particularly the placenta, which canbe obtained as part of prenatal diagnostic procedures or after birth,have provided one of the main avenues for such investigations. However,such tissues require invasive procedures.

The DNA methylation profile of the human placenta has intriguedresearchers for decades. The human placenta exhibits a plethora ofpeculiar physiological features involving DNA methylation. On a globallevel, placental tissues are hypomethylated when compared with mostsomatic tissues. At the gene level, the methylation status of selectedgenomic loci is a specific signature of placental tissues. Both theglobal and locus-specific methylation profiles show gestational-agedependent changes. Imprinted genes, namely genes for which expression isdependent on the parental origin of alleles serve key functions in theplacenta. The placenta has been described as pseudomalignant andhypermethylation of several tumor suppressor genes have been observed.

Studies of the DNA methylation profile of placental tissues haveprovided insights into the pathophysiology of pregnancy-associated ordevelopmentally-related diseases, such as preeclampsia and intrauterinegrowth restriction. Disorders in genomic imprinting are associated withdevelopmental disorders, such as Prader-Willi syndrome and Angelmansyndrome. Altered profiles of genomic imprinting and global DNAmethylation in placental and fetal tissues have been observed inpregnancies resulting from assisted reproductive techniques (H Hiura etal. 2012 Hum Reprod; 27: 2541-2548). A number of environmental factorssuch as maternal smoking (K E Haworth et al. 2013 Epigenomics; 5:37-49), maternal dietary factors (X Jiang et al. 2012 FASEB J; 26:3563-3574) and maternal metabolic status such as diabetes (N Hajj etal., Diabetes. doi: 10.2337/db12-0289) have been associated withepigenetic aberrations of the offsprings.

Despite decades of efforts, there had not been any practical meansavailable to study the fetal or tumor methylome and to monitor thedynamic changes throughout pregnancy or during disease processes, suchas malignancies. Therefore, it is desirable to provide methods foranalyzing all or portions of a fetal methylome and a tumor methylomenoninvasively.

SUMMARY

Embodiments provide systems, methods, and apparatuses for determiningand using methylation profiles of various tissues and samples. Examplesare provided. A methylation profile can be deduced for fetal/tumortissue based on a comparison of plasma methylation (or other sample withcell-free DNA, e.g., urine, saliva, genital washings) to a methylationprofile of the mother/patient. A methylation profile can be determinedfor fetal/tumor tissue using tissue-specific alleles to identify DNAfrom the fetus/tumor when the sample has a mixture of DNA. A methylationprofile can be used to determine copy number variations in genome of afetus/tumor. Methylation markers for a fetus have been identified viavarious techniques. The methylation profile can be determined bydetermining a size parameter of a size distribution of DNA fragments,where reference values for the size parameter can be used to determinemethylation levels.

Additionally, a methylation level can be used to determine a level ofcancer. In the context of cancer, the measurement of the methylomicchanges in plasma can allow one to detect the cancer (e.g. for screeningpurposes), for monitoring (e.g. to detect response following anti-cancertreatment; and to detect cancer relapse) and for prognostication (e.g.for measuring the load of cancer cells in the body or for stagingpurposes or for assessing the chance of death from disease or diseaseprogression or metastatic processes).

A better understanding of the nature and advantages of embodiments ofthe present invention may be gained with reference to the followingdetailed description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIGS. 1A1 and 1A2 show a table 100 of sequencing results for maternalblood, placenta, and maternal plasma according to embodiments of thepresent invention.

FIG. 1B shows methylation density in 1-Mb windows of sequenced samplesaccording to embodiments of the present invention.

FIGS. 2A-2C show plots of the beta-values against the methylationindices: (A) Maternal blood cells, (B) Chorionic villus sample, (C) Termplacental tissue.

FIGS. 3A and 3B show bar charts of percentage of methylated CpG sites inplasma and blood cells collected from an adult male and a non-pregnantadult female: (A) Autosomes, (B) Chromosome X.

FIGS. 4A and 4B show plots of methylation densities of correspondingloci in blood cell DNA and plasma DNA: (A) Non-pregnant adult female,(B) Adult male.

FIGS. 5A and 5B show bar charts of percentage of methylated CpG sitesamong samples collected from the pregnancy: (A). Autosomes, (B)Chromosome X.

FIG. 6 shows a bar chart of methylation level of different repeatclasses of the human genome for maternal blood, placenta and maternalplasma.

FIG. 7A shows a Circos plot 700 for first trimester samples. FIG. 7Bshows a Circos plot 750 for third trimester samples.

FIGS. 8A-8D show plots of comparisons of the methylation densities ofgenomic tissue DNA against maternal plasma DNA for CpG sites surroundingthe informative single nucleotide polymorphisms.

FIG. 9 is a flowchart illustrating a method 900 for determining a firstmethylation profile from a biological sample of an organism according toembodiments of the present invention.

FIG. 10 is a flowchart illustrating a method 1000 of determining a firstmethylation profile from a biological sample of an organism according toembodiments of the present invention.

FIGS. 11A and 11B show graphs of the performance of the predictingalgorithm using maternal plasma data and fractional fetal DNAconcentration according to embodiments of the present invention.

FIG. 12A is a table 1200 showing details of 15 selected genomic loci formethylation prediction according to embodiments of the presentinvention. FIG. 12B is a graph 1250 showing the deduced categories ofthe 15 selected genomic loci and their corresponding methylation levelsin the placenta.

FIG. 13 is a flowchart of a method 1300 for detecting a fetalchromosomal abnormality from a biological sample of a female subjectpregnant with at least one fetus.

FIG. 14 is a flowchart of a method 1400 for identifying methylationmarkers by comparing a placental methylation profile to a maternalmethylation profile according to embodiments of the present invention.

FIG. 15A is a table 1500 showing a performance of DMR identificationalgorithm using first trimester data with reference to 33 previouslyreported first trimester markers. FIG. 15B is a table 1550 showing aperformance of DMR identification algorithm using third trimester dataand compared with the placenta sample obtained at delivery.

FIG. 16 is a table 1600 showing the numbers of loci predicted to behypermethylated or hypomethylated based on direct analysis of thematernal plasma bisulfate-sequencing data.

FIG. 17A is a plot 1700 showing size distribution of maternal plasma,non-pregnant female control plasma, placental and peripheral blood DNA.FIG. 17B is a plot 1750 of size distribution and methylation profile ofmaternal plasma, adult female control plasma, placental tissue and adultfemale control blood.

FIGS. 18A and 18B are plots of methylation densities and size of plasmaDNA molecules according to embodiments of the present invention.

FIG. 19A shows a plot 1900 of methylation densities and the sizes ofsequenced reads for an adult non-pregnant female. FIG. 19B is a plot1950 showing size distribution and methylation profile of fetal-specificand maternal-specific DNA molecules in maternal plasma.

FIG. 20 is a flowchart of a method 2000 for estimating a methylationlevel of DNA in a biological sample of an organism according toembodiments of the present invention.

FIG. 21A is a table 2100 showing the methylation densities of thepre-operative plasma and the tissue samples of a hepatocellularcarcinoma (HCC) patient. FIG. 21B is a table 2150 showing the number ofsequence reads and the sequencing depth achieved per sample.

FIG. 22 is a table 2200 showing the methylation densities in theautosomes, ranging from 71.2% to 72.5%, in the plasma samples of thehealthy controls.

FIGS. 23A and 23B show methylation density of buffy coat, tumor tissue,non-tumoral liver tissue, the pre-operative plasma and post-operativeplasma of the HCC patient.

FIG. 24A is a plot 2400 showing the methylation densities of thepre-operative plasma from the HCC patient. FIG. 24B is a plot 2450showing the methylation densities of the post-operative plasma from theHCC patient.

FIGS. 25A and 25B show z-scores of the plasma DNA methylation densitiesfor the pre-operative (plot 2500) and post-operative (plot 2550) plasmasamples of the HCC patient using the plasma methylome data of the fourhealthy control subjects as reference for chromosome 1.

FIG. 26A is a table 2600 showing data for z-scores for pre-operative andpost-operative plasma. FIG. 26B is a Circos plot 2620 showing thez-score of the plasma DNA methylation densities for the pre-operativeand post-operative plasma samples of the HCC patient using the fourhealthy control subjects as reference for 1 Mb bins analyzed from allautosomes. FIG. 26C is a table 2640 showing a distribution of thez-scores of the 1 Mb bins for the whole genome in both the pre-operativeand post-operative plasma samples of the HCC patient. FIG. 26D is atable 2660 showing the methylation levels of the tumor tissue andpre-operative plasma sample overlapping with some of the control plasmasamples when using the CHH and CHG contexts.

FIGS. 27A-H show Circos plots of methylation density of 8 cancerpatients according to embodiments of the present invention. FIG. 27I istable 2780 showing the number of sequence reads and the sequencing depthachieved per sample. FIG. 27J is a table 2790 showing a distribution ofthe z-scores of the 1 Mb bins for the whole genome in plasma of patientswith different malignancies. CL=adenocarcinoma of lung;NPC=nasopharyngeal carcinoma; CRC=colorectal carcinoma;NE=neuroendocrine carcinoma; SMS=smooth muscle sarcoma.

FIG. 28 is a flowchart of method 2800 of analyzing a biological sampleof an organism to determine a classification of a level of canceraccording to embodiments of the present invention.

FIG. 29A is a plot 2900 showing the distribution of the methylationdensities in reference subjects assuming that this distribution followsa normal distribution. FIG. 29B is a plot 2950 showing the distributionof the methylation densities in cancer subjects assuming that thisdistribution follows a normal distribution and the mean methylationlevel is 2 standard deviations below the cutoff.

FIG. 30 is a plot 3000 showing the distribution of methylation densitiesof the plasma DNA of healthy subjects and cancer patients.

FIG. 31 is a graph 3100 showing the distribution of the differences inmethylation densities between the mean of the plasma DNA of healthysubjects and the tumor tissue of the HCC patient.

FIG. 32A is a table 3200 showing the effect of reducing the sequencingdepth when the plasma sample contained 5% or 2% tumor DNA.

FIG. 32B is a graph 3250 showing the methylation densities of the repeatelements and non-repeat regions in the plasma of the four healthycontrol subjects, the buffy coat, the normal liver tissue, the tumortissue, the pre-operative plasma and the post-operative plasma samplesof the HCC patient.

FIG. 33 shows a block diagram of an example computer system 3300 usablewith system and methods according to embodiments of the presentinvention.

FIG. 34A shows a size distribution of plasma DNA in the systemic lupuserythematosus (SLE) patient SLE04. FIGS. 34B and 34C show methylationanalysis for plasma DNA from a SLE patient SLE04 (FIG. 34B) and a HCCpatient TBR36 (FIG. 34C).

FIG. 35 is a flowchart of a method 3500 determining a classification ofa level of cancer based on hypermethylation of CpG islands according toembodiments of the present invention.

FIG. 36 is a flowchart of a method 3600 of analyzing a biological sampleof an organism using a plurality of chromosomal regions according toembodiments of the present invention.

FIG. 37A shows CNA analysis for tumor tissues, non-bisulfite(BS)-treated plasma DNA and bisulfate-treated plasma DNA (from inside tooutside) for patient TBR36. FIG. 37B is a scatter plot showing therelationship between the z-scores for the detection of CNA usingbisulfate- and non-bisulfate-treated plasma of the 1 Mb bins for thepatient TBR36.

FIG. 38A shows CNA analysis for tumor tissues, non-bisulfite(BS)-treated plasma DNA and bisulfate-treated plasma DNA (from inside tooutside) for patient TBR34. FIG. 38B is a scatter plot showing therelationship between the z-scores for the detection of CNA usingbisulfate-treated and non-bisulfate-treated plasma of the 1 Mb bins forthe patient TBR34.

FIG. 39A is a Circos plot showing the CNA (inner ring) and methylationanalysis (outer ring) for the bisulfite-treated plasma for a HCC patientTBR240. FIG. 39B is a Circos plot showing the CNA (inner ring) andmethylation analysis (outer ring) for the bisulfite-treated plasma for aHCC patient TBR164.

FIG. 40A shows the CNA analysis for patient TBR36 for the pre-treatmentsample and the post-treatment sample. FIG. 40B shows the methylationanalysis for patient TBR36 for the pre-treatment sample and thepost-treatment sample. FIG. 41A shows the CNA analysis for patient TBR34for the pre-treatment sample and the post-treatment sample. FIG. 41Bshows the methylation analysis for patient TBR34 for the pre-treatmentsample and the post-treatment sample.

FIG. 42 shows a diagram of diagnostic performance of genomewidehypomethylation analysis with different number of sequenced reads.

FIG. 43 is a diagram showing ROC curves for the detection of cancerbased on genomewide hypomethylation analysis with different bin sizes(50 kb, 100 kb, 200 kb and 1 Mb).

FIG. 44A shows a diagnostic performance for cumulative probability (CP)and percentage of bins with aberrations. FIG. 44B shows diagnosticperformances for the plasma analysis for global hypomethylation, CpGislands hypermethylation and CNA.

FIG. 45 shows a table with results for global hypomethylation, CpGislands hypermethylation and CNA in hepatocellular carcinoma patients.

FIG. 46 shows a table with results for global hypomethylation, CpGislands hypermethylation and CNA in patients suffering from cancersother than hepatocellular carcinoma.

FIG. 47 shows a serial analysis for plasma methylation for case TBR34.

FIG. 48A shows Circos plots demonstrating the CNA (inner ring) andmethylation changes (outer ring) in the bisulfite-treated plasma DNA forHCC patient TBR36. FIG. 48B is a plot of methylation z-scores forregions with chromosomal gains and loss, and regions without copy numberchange for the HCC patient TBR36.

FIG. 49A shows Circos plots demonstrating the CNA (inner ring) andmethylation changes (outer ring) in the bisulfite-treated plasma DNA forHCC patient TBR34. FIG. 49B is a plot of methylation z-scores forregions with chromosomal gains and loss, and regions without copy numberchange for the HCC patient TBR34.

FIGS. 50A and 50B show results of plasma hypomethylation and CNAanalysis for SLE patients SLE04 and SLE10.

FIGS. 51A and 51B show Z_(meth) analysis for regions with and withoutCNA for the plasma of two HCC patients (TBR34 and TBR36). FIGS. 51C and51D show Z_(meth) analysis for regions with and without CNA for theplasma of two SLE patients (SLE04 and SLE10).

FIG. 52A shows hierarchical clustering analysis for plasma samples fromHCC patients, non-HCC cancer patients and healthy control subjects usinggroup A features for CNA, global methylation, and CpG islandmethylation. FIG. 52B shows hierarchical clustering using group Bfeatures for CNA, global methylation, and CpG island methylation.

FIG. 53A shows hierarchical clustering analysis for plasma samples fromHCC patients, non-HCC cancer patients and healthy control subjects usingthe group A CpG islands methylation features. FIG. 53B showshierarchical clustering analysis for plasma samples from HCC patients,non-HCC cancer patients and healthy control subjects using the group Aglobal methylation densities.

FIG. 54A shows a hierarchical clustering analysis for plasma samplesfrom HCC patients, non-HCC cancer patients and healthy control subjectsusing the group A global CNAs. FIG. 54B shows a hierarchical clusteringanalysis for plasma samples from HCC patients, non-HCC cancer patientsand healthy control subjects using the group B CpG islands methylationdensities.

FIG. 55A shows a hierarchical clustering analysis for plasma samplesfrom HCC patients, non-HCC cancer patients and healthy control subjectsusing the group B global methylation densities. FIG. 55B shows ahierarchical clustering analysis for plasma samples from HCC patients,non-HCC cancer patients and healthy control subjects using the group Bglobal methylation densities.

FIG. 56 shows the mean methylation density of 1 Mb bins (red dots) among32 healthy subjects.

DEFINITIONS

A “methylome” provides a measure of an amount of DNA methylation at aplurality of sites or loci in a genome. The methylome may correspond toall of the genome, a substantial part of the genome, or relatively smallportion(s) of the genome. A “fetal methylome” corresponds to themethylome of a fetus of a pregnant female. The fetal methylome can bedetermined using a variety of fetal tissues or sources of fetal DNA,including placental tissues and cell-free fetal DNA in maternal plasma.A “tumor methylome” corresponds to the methylome of a tumor of anorganism (e.g., a human). The tumor methylome can be determined usingtumor tissue or cell-free tumor DNA in maternal plasma. The fetalmethylome and the tumor methylome are examples of a methylome ofinterest. Other examples of methylomes of interest are the methylomes oforgans (e.g. methylomes of brain cells, bones, the lungs, the heart, themuscles and the kidneys, etc.) that can contribute DNA into a bodilyfluid (e.g. plasma, serum, sweat, saliva, urine, genital secretions,semen, stools fluid, diarrheal fluid, cerebrospinal fluid, secretions ofthe gastrointestinal tract, pancreatic secretions, intestinalsecretions, sputum, tears, aspiration fluids from breast and thyroid,etc.). The organs may be transplanted organs.

A “plasma methylome” is the methylome determined from the plasma orserum of an animal (e.g., a human). The plasma methylome is an exampleof a cell-free methylome since plasma and serum include cell-free DNA.The plasma methylome is also an example of a mixed methylome since it isa mixture of fetal/maternal methylome or tumor/patient methylome. The“placental methylome” can be determined from a chorionic villus sample(CVS) or a placental tissue sample (e.g., obtained following delivery).The “cellular methylome” corresponds to the methylome determined fromcells (e.g., blood cells) of the patient. The methylome of the bloodcells is called the blood cell methylome (or blood methylome).

A “site” corresponds to a single site, which may be a single baseposition or a group of correlated base positions, e.g., a CpG site. A“locus” may correspond to a region that includes multiple sites. A locuscan include just one site, which would make the locus equivalent to asite in that context.

The “methylation index” for each genomic site (e.g., a CpG site) refersto the proportion of sequence reads showing methylation at the site overthe total number of reads covering that site. The “methylation density”of a region is the number of reads at sites within the region showingmethylation divided by the total number of reads covering the sites inthe region. The sites may have specific characteristics, e.g., being CpGsites. Thus, the “CpG methylation density” of a region is the number ofreads showing CpG methylation divided by the total number of readscovering CpG sites in the region (e.g., a particular CpG site, CpG siteswithin a CpG island, or a larger region). For example, the methylationdensity for each 100-kb bin in the human genome can be determined fromthe total number of cytosines not converted after bisulfite treatment(which corresponds to methylated cytosine) at CpG sites as a proportionof all CpG sites covered by sequence reads mapped to the 100-kb region.This analysis can also be performed for other bin sizes, e.g. 50-kb or1-Mb, etc. A region could be the entire genome or a chromosome or partof a chromosome (e.g. a chromosomal arm). The methylation index of a CpGsite is the same as the methylation density for a region when the regiononly includes that CpG site. The “proportion of methylated cytosines”refers the number of cytosine sites, “C's”, that are shown to bemethylated (for example unconverted after bisulfite conversion) over thetotal number of analyzed cytosine residues, i.e. including cytosinesoutside of the CpG context, in the region. The methylation index,methylation density and proportion of methylated cytosines are examplesof “methylation levels.”

A “methylation profile” (also called methylation status) includesinformation related to DNA methylation for a region. Information relatedto DNA methylation can include, but not limited to, a methylation indexof a CpG site, a methylation density of CpG sites in a region, adistribution of CpG sites over a contiguous region, a pattern or levelof methylation for each individual CpG site within a region thatcontains more than one CpG site, and non-CpG methylation. A methylationprofile of a substantial part of the genome can be considered equivalentto the methylome. “DNA methylation” in mammalian genomes typicallyrefers to the addition of a methyl group to the 5′ carbon of cytosineresidues (i.e. 5-methylcytosines) among CpG dinucleotides. DNAmethylation may occur in cytosines in other contexts, for example CHGand CHH, where H is adenine, cytosine or thymine. Cytosine methylationmay also be in the form of 5-hydroxymethylcytosine. Non-cytosinemethylation, such as N6-methyladenine, has also been reported.

A “tissue” corresponds to any cells. Different types of tissue maycorrespond to different types of cells (e.g., liver, lung, or blood),but also may correspond to tissue from different organisms (mother vs.fetus) or to healthy cells vs. tumor cells. A “biological sample” refersto any sample that is taken from a subject (e.g., a human, such as apregnant woman, a person with cancer, or a person suspected of havingcancer, an organ transplant recipient or a subject suspected of having adisease process involving an organ (e.g., the heart in myocardialinfarction, or the brain in stroke) and contains one or more nucleicacid molecule(s) of interest. The biological sample can be a bodilyfluid, such as blood, plasma, serum, urine, vaginal fluid, uterine orvaginal flushing fluids, plural fluid, ascitic fluid, cerebrospinalfluid, saliva, sweat, tears, sputum, bronchioalveolar lavage fluid, etc.Stool samples can also be used.

The term “level of cancer” can refer to whether cancer exists, a stageof a cancer, a size of tumor, whether there is metastasis, the totaltumor burden of the body, and/or other measure of a severity of acancer. The level of cancer could be a number or other characters. Thelevel could be zero. The level of cancer also includes premalignant orprecancerous conditions (states) associated with mutations or a numberof mutations. The level of cancer can be used in various ways. Forexample, screening can check if cancer is present in someone who is notknown previously to have cancer. Assessment can investigate someone whohas been diagnosed with cancer to monitor the progress of cancer overtime, study the effectiveness of therapies or to determine theprognosis. In one embodiment, the prognosis can be expressed as thechance of a patient dying of cancer, or the chance of the cancerprogressing after a specific duration or time, or the chance of cancermetastasizing. Detection can mean ‘screening’ or can mean checking ifsomeone, with suggestive features of cancer (e.g. symptoms or otherpositive tests), has cancer.

DETAILED DESCRIPTION

Epigenetic mechanisms play an important role in embryonic and fetaldevelopment. However, human embryonic and fetal tissues (includingplacental tissues) are not readily accessible (U.S. Pat. No. 6,927,028).Certain embodiments have addressed this problem by analyzing a samplethat has cell-free fetal DNA molecules present in maternal circulation.The fetal methylome can be deduced in a variety of ways. For example,the maternal plasma methylome can be compared to a cellular methylome(from blood cells of the mother) and the difference is shown to becorrelated to the fetal methylome. As another example, fetal-specificalleles can be used to determine the methylation of the fetal methylomeat specific loci. Additionally, the size of a fragment can be used as anindicator of a methylation percentage, as a correlation between size andmethylation percentage is shown.

In one embodiment, genome-wide bisulfate sequencing is used to analyzethe methylation profile (part or all of a methylome) of maternal plasmaDNA at single nucleotide resolution. By exploiting the polymorphicdifferences between the mother and the fetus, the fetal methylome couldbe assembled from maternal blood samples. In another implementation,polymorphic differences were not used, but a differential between theplasma methylome and the blood cell methylome can be used.

In another embodiment, by exploiting single nucleotide variations and/orcopy number aberrations between a tumor genome and a nontumor genome,and sequencing data from plasma (or other sample), methylation profilingof a tumor can be performed in the sample of a patient suspected orknown to have cancer. A difference in a methylation level in a plasmasample of a test individual when compared with the plasma methylationlevel of a healthy control or a group of healthy controls can allow theidentification of the test individual as harboring cancer. Additionally,the methylation profile can act as a signature that reveals the type ofcancer, for example, from which organ, that the person has developed andwhether metastasis has occurred.

Due to the noninvasive nature of this approach, we were able to seriallyassess the fetal and maternal plasma methylomes from maternal bloodsamples collected in the first trimester, third trimester and afterdelivery. Gestation-related changes were observed. The approach can alsobe applied to samples obtained during the second trimester. The fetalmethylome deduced from maternal plasma during pregnancy resembled theplacental methylome. Imprinted genes and differentially methylatedregions were identified from the maternal plasma data.

We have therefore developed an approach to study the fetal methylomenoninvasively, serially and comprehensively, thus offering thepossibility for identifying biomarkers or direct testing ofpregnancy-related pathologies. Embodiments can also be used to study thetumor methylome noninvasively, serially and comprehensively, forscreening or detecting if a subject is suffering from cancer, formonitoring malignant diseases in a cancer patient and forprognostication. Embodiments can be applied to any cancer type,including, but not limited to, lung cancer, breast cancer, colorectalcancer, prostate cancer, nasopharyngeal cancer, gastric cancer,testicular cancer, skin cancer (e.g. melanoma), cancer affecting thenervous system, bone cancer, ovarian cancer, liver cancer (e.g.hepatocellular carcinoma), hematologic malignancies, pancreatic cancer,endometriocarcinoma, kidney cancer, cervical cancer, bladder cancer,etc.

A description of how to determine a methylome or methylation profile isfirst discussed, and then different methylomes are described (such asfetal methylomes, a tumor methylome, methylomes of the mother or apatient, and a mixed methylome, e.g., from plasma). The determination ofa fetal methylation profile is then described using fetal-specificmarkers or by comparing a mixed methylation profile to a cellularmethylation profile. Fetal methylation markers are determined bycomparing methylation profiles. A relationship between size andmethylation is discussed. Uses of methylation profiles to detect cancerare also provided.

I. Determination of a Methylome

A myriad of approaches have been used to investigate the placentalmethylome, but each approach has its limitations. For example, sodiumbisulfite, a chemical that modifies unmethylated cytosine residues touracil and leaves methylated cytosine unchanged, converts thedifferences in cytosine methylation into a genetic sequence differencefor further interrogation. The gold standard method of studying cytosinemethylation is based on treating tissue DNA with sodium bisulfitefollowed by direct sequencing of individual clones ofbisulfite-converted DNA molecules. After the analysis of multiple clonesof DNA molecules, the cytosine methylation pattern and quantitativeprofile per CpG site can be obtained. However, cloned bisulfitesequencing is a low throughput and labor-intensive procedure that cannotbe readily applied on a genome-wide scale.

Methylation-sensitive restriction enzymes that typically digestunmethylated DNA provide a low cost approach to study DNA methylation.However, data generated from such studies are limited to loci with theenzyme recognition motifs and the results are not quantitative.Immunoprecipitation of DNA bound by anti-methylated cytosine antibodiescan be used to survey large segments of the genome but tends to biastowards loci with dense methylation due to higher strength of antibodybinding to such regions. Microarray-based approaches are dependent onthe a priori design of the interrogation probes and hybridizationefficiencies between the probes and the target DNA.

To interrogate a methylome comprehensively, some embodiments usemassively parallel sequencing (MPS) to provide genome-wide informationand quantitative assessment of the level of methylation on a pernucleotide and per allele basis. Recently, bisulfite conversion followedby genome-wide MPS has become feasible (R Lister et al 2008 Cell; 133:523-536).

Among the small number of published studies (R Lister et al. 2009Nature; 462: 315-322; L Laurent et al. 2010 Genome Res; 20: 320-331; YLi et al. 2010 PLoS Biol; 8: e1000533; and M Kulis et al. 2012 NatGenet; 44: 1236-1242) that applied genome-wide bisulfite sequencing forthe investigation of human methylomes, two studies focused on embryonicstem cells and fetal fibroblasts (R Lister et al. 2009 Nature; 462:315-322; L Laurent et al. 2010 Genome Res; 20: 320-331). Both studiesanalyzed cell-line derived DNA.

A. Genome-Wide Bisulfite Sequencing

Certain embodiments can overcome the aforesaid challenges and enableinterrogation of a fetal methylome comprehensively, noninvasively andserially. In one embodiment, genome-wide bisulfite sequencing was usedto analyze cell-free fetal DNA molecules that are found in thecirculation of pregnant women. Despite the low abundance and fragmentednature of plasma DNA molecules, we were able to assemble a highresolution fetal methylome from maternal plasma and serially observe thechanges with pregnancy progression. Given the intense interest innoninvasive prenatal testing (NIPT), embodiments can provide a powerfulnew tool for fetal biomarker discovery or serve as a direct platform forachieving NIPT of fetal or pregnancy-associated diseases. Data from thegenome-wide bisulfite sequencing of various samples, from which thefetal methylome can be derived, is now provided. In one embodiment, thistechnology can be applied for methylation profiling in pregnanciescomplicated with preeclampsia, or intrauterine growth retardation, orpreterm labor. For such complicated pregnancies, this technology can beused serially because of its noninvasive nature, to allow for themonitoring and/or prognostication and/or response to treatment.

FIGS. 1A1 and 1A2 show a table 100 of sequencing results for maternalblood, placenta, and maternal plasma according to embodiments of thepresent invention. In one embodiment, whole genome sequencing wasperformed on bisulfite-converted DNA libraries, prepared usingmethylated DNA library adaptors (Illumina) (R Lister et al. 2008 Cell;133: 523-536), of blood cells of the blood sample collected in the firsttrimester, the CVS, the placental tissue collected at term, the maternalplasma samples collected during the first and third trimesters and thepostpartum period. Blood cell and plasma DNA samples obtained from oneadult male and one adult non-pregnant female were also analyzed. A totalof 9.5 billion pairs of raw sequence reads were generated in this study.The sequencing coverage of each sample is shown in table 100.

The sequence reads that were uniquely mappable to the human referencegenome reached average haploid genomic coverages of 50 folds, 34 foldsand 28 folds, respectively, for the first trimester, third trimester andpost-delivery maternal plasma samples. The coverage of the CpG sites inthe genome ranged from 81% to 92% for the samples obtained from thepregnancy. The sequence reads that spanned CpG sites amounted to averagehaploid coverages of 33 folds per strand, 23 folds per strand and 19folds per strand, respectively, for the first trimester, third trimesterand post-delivery maternal plasma samples. The bisulfite conversionefficiencies for all samples were >99.9% (table 100).

In table 100, ambiguous rate (marked “a”) refers to the proportion ofreads mapped onto both the Watson and Crick strands of the referencehuman genome. Lambda conversion rate refers to the proportion ofunmethylated cytosines in the internal lambda DNA control beingconverted to the “thymine” residues by bisulfite modification. Hgenerically equates to A, C, or T. “a” refers to reads that could bemapped to a specific genomic locus but cannot be assigned to the Watsonor Crick strand. “b” refers to paired reads with identical start and endcoordinates. For “c”, lambda DNA was spiked into each sample beforebisulfite conversion. The lambda conversion rate refers to theproportion of cytosine nucleotides that remain as cytosine afterbisulfite conversion and is used as an indication of the rate ofsuccessful bisulfite conversion. “d” refers to the number of cytosinenucleotides present in the reference human genome and remaining as acytosine sequence after bisulfite conversion.

During bisulfite modification, unmethylated cytosines are converted touracils and subsequently thymines after PCR amplifications while themethylated cytosines would remain intact (M Frommer et al. 1992 ProcNatl Acad Sci USA; 89:1827-31). After sequencing and alignment, themethylation status of an individual CpG site could thus be inferred fromthe count of methylated sequence reads “M” (methylated) and the count ofunmethylated sequence reads “U” (unmethylated) at the cytosine residuein CpG context. Using the bisulfite sequencing data, the entiremethylomes of maternal blood, placenta and maternal plasma wereconstructed. The mean methylated CpG density (also called methylationdensity MD) of specific loci in the maternal plasma can be calculatedusing the equation:

${MD} = \frac{M}{M + U}$

where M is the count of methylated reads and U is the count ofunmethylated reads at the CpG sites within the genetic locus. If thereis more than one CpG site within a locus, then M and U correspond to thecounts across the sites.

B. Various Techniques

As described above, methylation profiling can be performed usingmassively parallel sequencing (MPS) of bisulfite converted plasma DNA.The MPS of the bisulfite converted plasma DNA can be performed in arandom or shotgun fashion. The depth of the sequencing can be variedaccording to the size of the region of interest.

In another embodiment, the region(s) of interest in the bisulfiteconverted plasma DNA can be first captured using a solution-phase orsolid-phase hybridization-based process, followed by the MPS. Themassively parallel sequencing can be performed using asequencing-by-synthesis platform such as the Illumina, asequencing-by-ligation platform such as the SOLiD platform from LifeTechnologies, a semiconductor-based sequencing system such as the IonTorrent or Ion Proton platforms from Life Technologies, or singlemolecule sequencing system such as the Helicos system or the PacificBiosciences system or a nanopore-based sequencing system. Nanopore-basedsequencing including nanopores that are constructed using, for example,lipid bilayers and protein nanopore, and solid-state nanopores (such asthose that are graphene based). As selected single molecule sequencingplatforms would allow the methylation status of DNA molecules (includingN6-methyladenine, 5-methylcytosine and 5-hydroxymethylcytosine) to beelucidated directly without bisulfite conversion (B A Flusberg et al.2010 Nat Methods; 7: 461-465; J Shim et al. 2013 Sci Rep; 3:1389. doi:10.1038/srep01389), the use of such platforms would allow themethylation status of non-bisulfite converted sample DNA (e.g. plasmaDNA) to be analyzed.

Besides sequencing, other techniques can be used. In one embodiment,methylation profiling can be done by methylation-specific PCR ormethylation-sensitive restriction enzyme digestion followed by PCR orligase chain reaction followed by PCR. In yet other embodiments, the PCRis a form of single molecule or digital PCR (B Vogelstein et al. 1999Proc Natl Acad Sci USA; 96: 9236-9241). In yet further embodiments, thePCR can be a real-time PCR. In other embodiments, the PCR can bemultiplex PCR.

II. Analysis of Methylomes

Some embodiments can determine the methylation profile of plasma DNAusing whole genome bisulfite sequencing. The methylation profile of afetus can be determined by sequencing maternal plasma DNA samples, as isdescribed below. Thus, the fetal DNA molecules (and fetal methylome)were accessed noninvasively during the pregnancy, and changes weremonitored serially as the pregnancy progressed. Due to thecomprehensiveness of the sequencing data, we were able to study thematernal plasma methylomes on a genome-wide scale at single nucleotideresolution.

Since the genomic coordinates of the sequenced reads were known, thesedata enabled one to study the overall methylation levels of themethylome or any region of interest in the genome and to make comparisonbetween different genetic elements. In addition, multiple sequence readscovered each CpG site or locus. A description of some of the metricsused to measure the methylome is now provided.

A. Methylation of Plasma DNA Molecules

DNA molecules are present in human plasma at low concentrations and in afragmented form, typically in lengths resembling mononucleosomal units(Y M D Lo et al. 2010 Sci Transl Med; 2: 61ra91; and Y W Zheng at al.2012 Clin Chem; 58: 549-558). Despite these limitations, a genome-widebisulfite-sequencing pipeline was able to analyze the methylation of theplasma DNA molecules. In yet other embodiments, as selected singlemolecule sequencing platforms would allow the methylation status of DNAmolecules to be elucidated directly without bisulfite conversion (BAFlusberg et al. 2010 Nat Methods; 7: 461-465; J Shim et al. 2013 SciRep; 3:1389. doi: 10.1038/srep01389), the use of such platforms wouldallow the non-bisulfite converted plasma DNA to be used to determine themethylation levels of plasma DNA or to determine the plasma methylome.Such platforms can detect N6-methyladenine, 5-methylcytosine, and5-hydroxymethylcytosine, which can provide improved results (e.g.,improved sensitivity or specificity) related to the different biologicalfunctions of the different forms of methylation. Such improved resultscan be useful when applying embodiments for the detection or monitoringof specific disorders, e.g. preeclampsia or a particular type of cancer.

Bisulfite sequencing can also discriminate between different forms ofmethylation. In one embodiment, one can include additional steps thatcan distinguish 5-methylcytosine from 5-hydroxymethylcytosine. One suchapproach is oxidative bisulfite sequencing (oxBS-seq), which canelucidate the location of 5-methylcytosine and 5-hydroxymethylcytosineat single-base resolution (MJ Booth et al. 2012 Science; 336: 934-937;MJ Booth et al. 2013 Nature Protocols; 8: 1841-1851). In bisulfitesequencing, both 5-methylcytosine from 5-hydroxymethylcytosine are readas cytosines and thus cannot be discriminated. On the other hand, inoxBS-seq, specific oxidation of 5-hydroxymethylcytosine to5-formylcytosine by treatment with potassium perruthenate (KRuO4),followed by the conversion of the newly formed 5-formylcytosine touracil using bisulfite conversion would allow 5-hydroxymethylcytosine tobe distinguished from 5-methylcytosine. Hence, a readout of5-methylcytosine can be obtained from a single oxBS-seq run, and5-hydroxymethylcytosine levels are deduced by comparison with thebisulfite sequencing results. In another embodiment, 5-methylcytosinecan be distinguished from 5-hydroxymethylcytosine using Tet-assistedbisulfite sequencing (TAB-seq) (M Yu et al. 2012 Nat Protoc; 7:2159-2170). TAB-seq can identify 5-hydroxymethylcytosine at single-baseresolution, as well as determine its abundance at each modificationsite. This method involves (3-glucosyltransferase-mediated protection of5-hydroxymethylcytosine (glucosylation) and recombinant mouseTet1(mTet1)-mediated oxidation of 5-methylcytosine to5-carboxylcytosine. After the subsequent bisulfite treatment and PCRamplification, both cytosine and 5-carboxylcytosine (derived from5-methylcytosine) are converted to thymine (T), whereas5-hydroxymethylcytosine will be read as C.

FIG. 1B shows methylation density in 1-Mb windows of sequenced samplesaccording to embodiments of the present invention. Plot 150 is a Circosplot depicting the methylation density in the maternal plasma andgenomic DNA in 1-Mb windows across the genome. From outside to inside:chromosome ideograms can be oriented pter-qter in a clockwise direction(centromeres are shown in red), maternal blood (red), placenta (yellow),maternal plasma (green), shared reads in maternal plasma (blue), andfetal-specific reads in maternal plasma (purple). The overall CpGmethylation levels (i.e., density levels) of maternal blood cells,placenta and maternal plasma can be found in table 100. The methylationlevel of maternal blood cells is in general higher than that of theplacenta across the whole genome.

B. Comparison of Bisulfite Sequencing to Other Techniques

We studied the placental methylome using massively parallel bisulfitesequencing. In addition, we studied the placental methylome using anoligonucleotide array platform that covered about 480,000 CpG sites inthe human genome (Illumina) (M Kulis et al. 2012 Nat Genet; 44:1236-1242; and C Clark et al. 2012 PLoS One; 7: e50233). In oneembodiment using beadchip-based genotyping and methylation analysis,genotyping was performed using the Illumina HumanOmni2.5-8 genotypingarray according to the manufacturer's protocol. Genotypes were calledusing the GenCall algorithm of the Genome Studio Software (Illumina).The call rates were over 99%. For the microarray based methylationanalysis, genomic DNA (500-800 ng) was treated with sodium bisulfiteusing the Zymo EZ DNA Methylation Kit (Zymo Research, Orange, Calif.,USA) according to the manufacturer's recommendations for the IlluminaInfinium Methylation Assay.

The methylation assay was performed on 4 μl bisulfite-converted genomicDNA at 50 ng/μl according to the Infinium HD Methylation Assay protocol.The hybridized beadchip was scanned on an Illumina iScan instrument. DNAmethylation data were analyzed by the GenomeStudio (v2011.1) MethylationModule (v1.9.0) software, with normalization to internal controls andbackground subtraction. The methylation index for individual CpG sitewas represented by a beta value (β), which was calculated using theratio of fluorescent intensities between methylated and unmethylatedalleles:

$\beta = \frac{{Intensity}\mspace{14mu}{of}\mspace{14mu}{methylated}\mspace{14mu}{allele}}{\begin{matrix}{{{Intensity}\mspace{14mu}{of}\mspace{14mu}{unmethylated}\mspace{14mu}{allele}} +} \\{{{Intensity}\mspace{14mu}{of}\mspace{14mu}{methylated}\mspace{14mu}{allele}} + 100}\end{matrix}}$

For CpG sites that were represented on the array and sequenced tocoverage of at least 10 folds, we compared the beta-value obtained bythe array to the methylation index as determined by sequencing of thesame site. Beta-values represented the intensity of methylated probes asa proportion of the combined intensity of the methylated andunmethylated probes covering the same CpG site. The methylation indexfor each CpG site refers to the proportion of methylated reads over thetotal number of reads covering that CpG.

FIGS. 2A-2C show plots of the beta-values determined by the IlluminaInfinium HumanMethylation 450K beadchip array against the methylationindices determined by genome-wide bisulfite sequencing of correspondingCpG sites that were interrogated by both platforms: (A) Maternal bloodcells, (B) Chorionic villus sample, (C) Term placental tissue. The datafrom both platforms were highly concordant and the Pearson correlationcoefficients were 0.972, 0.939 and 0.954, and R² values were 0.945,0.882 and 0.910 for the maternal blood cells, CVS and term placentaltissue, respectively.

We further compared our sequencing data with those reported by Chu etal, who investigated the methylation profiles of 12 pairs of CVS andmaternal blood cell DNA samples using an oligonucleotide array thatcovered about 27,000 CpG sites (T Chu et al. 2011 PLoS One; 6: e14723).The correlation data between the sequencing results of the CVS andmaternal blood cell DNA and each of the 12 pairs of samples in theprevious study gave an average Pearson coefficient (0.967) and R²(0.935) for maternal blood and an average Pearson coefficient (0.943)and R² (0.888) for the CVS. Among the CpG sites represented on botharrays, our data correlated highly with the published data. The rates ofnon-CpG methylation were <1% for the maternal blood cells, CVS andplacental tissues (table 100). These results were consistent withcurrent belief that substantial amounts of non-CpG methylation weremainly restricted to pluripotent cells (R Lister et al. 2009 Nature;462: 315-322; L Laurent et al. 2010 Genome Res; 20: 320-331).

C. Comparison of Plasma and Blood Methylomes for Non Pregnant Subjects

FIGS. 3A and 3B show bar charts of percentage of methylated CpG sites inplasma and blood cells collected from an adult male and a non-pregnantadult female: (A) Autosomes, (B) Chromosome X. The charts show asimilarity between plasma and blood methylomes of a male and anon-pregnant female. The overall proportions of CpG sites that weremethylated in the male and non-pregnant female plasma samples werealmost the same as the corresponding blood cell DNA (table 100 and FIGS.2A and 2B).

We next studied the correlation of the methylation profiles of theplasma and blood cell samples in a locus-specific manner. We determinedthe methylation density of each 100-kb bin in the human genome bydetermining the total number of unconverted cytosines at CpG sites as aproportion of all CpG sites covered by sequence reads mapped to the100-kb region. The methylation densities were highly concordant betweenthe plasma sample and corresponding blood cell DNA of the male as wellas the female samples.

FIGS. 4A and 4B show plots of methylation densities of correspondingloci in blood cell DNA and plasma DNA: (A) Non-pregnant adult female,(B) Adult male. The Pearson correlation coefficient and R² value for thenon-pregnant female samples were respectively 0.963 and 0.927, and thatfor the male samples were respectively 0.953 and 0.908. These data areconsistent with previous findings based on the assessment of genotypesof plasma DNA molecules of recipients of allogenic hematopoietic stemcell transplantation which showed that hematopoietic cells are thepredominant source of DNA in human plasma (Y W Zheng at al. 2012 ClinChem; 58: 549-558).

D. Methylation Levels Across Methylomes

We next studied the DNA methylation levels of maternal plasma DNA,maternal blood cells, and placental tissue to determine methylationlevels. The levels were determined for repeat regions, non-repeatregions, and overall.

FIGS. 5A and 5B show bar charts of percentage of methylated CpG sitesamong samples collected from the pregnancy: (A). Autosomes, (B)Chromosome X. The overall proportions of methylated CpGs were 67.0% and68.2% for the first and third trimester maternal plasma samples,respectively. Unlike the results obtained from the non-pregnantindividuals, these proportions were lower than that of the firsttrimester maternal blood cell sample but higher than that of the CVS andterm placental tissue samples (table 100). Of note, the percentage ofmethylated CpGs for the post-delivery maternal plasma sample was 73.1%which was similar to the blood cell data (table 100). These trends wereobserved in CpGs distributed over all autosomes as well as chromosome Xand spanned across both the non-repeat regions and multiple classes ofrepeat elements of the human genome.

Both the repeat and non-repeat elements in the placenta were found to behypomethylated relative to maternal blood cells. The results wereconcordant to the findings in the literature that the placenta ishypomethylated relative to other tissues, including peripheral bloodcells.

Between 71% to 72% of the sequenced CpG sites were methylated in theblood cell DNA from the pregnant woman, non-pregnant woman and adultmale (table 100 of FIG. 1). These data are comparable with the report of68.4% of CpG sites of blood mononuclear cells reported by Y Li et al.2010 PLoS Biol; 8: e1000533. Consistent with the previous reports on thehypomethylated nature of placental tissues, 55% and 59% of the CpG siteswere methylated in the CVS and term placental tissue, respectively(table 100).

FIG. 6 shows a bar chart of methylation level of different repeatclasses of the human genome for maternal blood, placenta and maternalplasma. The repeat classes are as defined by the UCSC genome browser.Data shown are from the first trimester samples. Unlike earlier datasuggesting that the hypomethylated nature of placental tissues wasmainly observed in certain repeat classes in the genome (B Novakovic etal. 2012 Placenta; 33: 959-970), here we show that the placenta was infact hypomethylated in most classes of genomic elements with referenceto blood cells.

E. Similarity of Methylomes

Embodiments can determine the methylomes of placental tissues, bloodcells and plasma using the same platform. Hence, direct comparisons ofthe methylomes of those biological sample types were possible. The highlevel of resemblance between methylomes of the blood cells and plasmafor the male and non-pregnant female as well as between the maternalblood cells and the post-delivery maternal plasma sample furtheraffirmed that hematopoietic cells were the main sources of DNA in humanplasma (Y W Zheng at al. 2012 Clin Chem; 58: 549-558).

The resemblances are evident both in terms of the overall proportion ofmethylated CpGs in the genome as well as from the high correlation ofmethylation densities between corresponding loci in the blood cell DNAand plasma DNA. Yet, the overall proportions of methylated CpGs in thefirst trimester and third trimester maternal plasma samples were reducedwhen compared with the maternal blood cell data or the post-deliverymaternal plasma sample. The reduced methylation levels during pregnancywere due to the hypomethylated nature of the fetal DNA molecules presentin maternal plasma.

The reversal of the methylation profile in the post-delivery maternalplasma sample to become more similar to that of the maternal blood cellssuggests that the fetal DNA molecules had been removed from the maternalcirculation. Calculation of the fetal DNA concentrations based on SNPmarkers of the fetus indeed showed that the concentration changed from33.9% before delivery to just 4.5% in the post-delivery sample.

F. Other Applications

Embodiments have successfully assembled DNA methylomes through the MPSanalysis of plasma DNA. The ability to determine the placental or fetalmethylome from maternal plasma provides a noninvasive method todetermine, detect and monitor the aberrant methylation profilesassociated with pregnancy-associated conditions such as preeclampsia,intrauterine growth restriction, preterm labor and others. For example,the detection of a disease-specific aberrant methylation signatureallows the screening, diagnosis and monitoring of suchpregnancy-associated conditions. The measuring of the maternal plasmamethylation level allows the screening, diagnosis and monitoring of suchpregnancy-associated conditions. Besides the direct applications on theinvestigation of pregnancy-associated conditions, the approach could beapplied to other areas of medicine where plasma DNA analysis is ofinterest. For example, the methylomes of cancers could be determinedfrom plasma DNA of cancer patients. Cancer methylomic analysis fromplasma, as described herein, is potentially a synergistic technology tocancer genomic analysis from plasma (K C A Chan at al. 2013 Clin Chem;59: 211-224 and R J Leary et al. 2012 Sci Transl Med; 4:162ra154).

For example, the determination of a methylation level of a plasma samplecould be used to screen for cancer. When the methylation level of theplasma sample shows aberrant levels compared with healthy controls,cancer may be suspected. Then further confirmation and assessment of thetype of cancer or tissue origin of the cancer may be performed bydetermining the plasma profile of methylation at different genomic locior by plasma genomic analysis to detect tumor-associated copy numberaberrations, chromosomal translocations and single nucleotide variants.Indeed, in one embodiment of this invention, the plasma cancermethylomic and genomic profiling can be carried out simultaneously.Alternatively, radiological and imaging investigations (e.g. computedtomography, magnetic resonance imaging, positron emission tomography) orendoscopy (e.g. upper gastrointestinal endoscopy or colonoscopy) couldbe used to further investigate individuals who were suspected of havingcancer based on the plasma methylation level analysis.

For cancer screening or detection, the determination of a methylationlevel of a plasma (or other biologic) sample can be used in conjunctionwith other modalities for cancer screening or detection such as prostatespecific antigen measurement (e.g. for prostate cancer),carcinoembryonic antigen (e.g. for colorectal carcinoma, gastriccarcinoma, pancreatic carcinoma, lung carcinoma, breast carcinoma,medullary thyroid carcinoma), alpha fetoprotein (e.g. for liver canceror germ cell tumors), CA125 (e.g. for ovarian and breast cancer) andCA19-9 (e.g. for pancreatic carcinoma).

Additionally, other tissues may be sequenced to obtain a cellularmethylome. For example, liver tissue can be analyzed to determine amethylation pattern specific to the liver, which may be used to identifyliver pathologies. Other tissues which can also be analyzed includebrain cells, bones, the lungs, the heart, the muscles and the kidneys,etc. The methylation profiles of various tissues may change from time totime, e.g. as a result of development, aging, disease processes (e.g.inflammation or cirrhosis or autoimmune processes (such as in systemiclupus erythematosus)) or treatment (e.g. treatment with demethylatingagents such as 5-azacytidine and 5-azadeoxycytidine). The dynamic natureof DNA methylation makes such analysis potentially very valuable formonitoring of physiological and pathological processes. For example, ifone detects a change in the plasma methylome of an individual comparedto a baseline value obtained when they were healthy, one could thendetect disease processes in organs that contribute plasma DNA.

Also, the methylomes of transplanted organs could be determined fromplasma DNA of organ transplantation recipients. Transplant methylomicanalysis from plasma, as described in this invention, is potentially asynergistic technology to transplant genomic analysis from plasma (Y WZheng at al, 2012; Y M D Lo at al. 1998 Lancet; 351: 1329-1330; and T MSnyder et al. 2011 Proc Natl Acad Sci USA; 108: 6229-6234). As plasmaDNA is generally regarded as a marker of cell death, an increase in theplasma level of DNA released from a transplanted organ could be used asa marker for increased cell death from that organ, such as a rejectionepisode or other pathologic processes involving that organ (e.g.infection or abscess). In the event that anti-rejection therapy issuccessfully instituted, the plasma level of DNA released by thetransplanted organ will be expected to reduce.

III. Determining Fetal or Tumor Methylome Using SNPs

As described above, the plasma methylome corresponds to the bloodmethylome for a non-pregnant normal person. However, for a pregnantfemale, the methylomes differ. Fetal DNA molecules circulate in maternalplasma among a majority background of maternal DNA (Y M D Lo et al. 1998Am J Hum Genet; 62: 768-775). Thus, for a pregnant female, the plasmamethylome is largely a composite of the placental methylome and theblood methylome. Accordingly, one can extract the placental methylomefrom plasma.

In one embodiment, single nucleotide polymorphism (SNP) differencesbetween the mother and the fetus are used to identify the fetal DNAmolecules in maternal plasma. An aim was to identify SNP loci where themother is homozygous, but the fetus is heterozygous; the fetal-specificallele can be used to determine which DNA fragments are from the fetus.Genomic DNA from the maternal blood cells was analyzed using a SNPgenotyping array, the Illumina HumanOmni2.5-8. On the other hand, forSNP loci in which the mother is heterozygous and the fetus ishomozygous, then the SNP allele that is specific to the mother can beused to determine which plasma DNA fragments are from the mother. Themethylation level of such DNA fragments would be reflective of themethylation level for the related genomic regions in the mother.

A. Correlation of Methylation of Fetal-Specific Reads and PlacentalMethylome

Loci having two different alleles, where the amount of one allele (B)was significantly less than the other allele (A), were identified fromsequencing results of a biological sample. Reads covering the B alleleswere regarded as fetal-specific (fetal-specific reads). The mother isdetermined to be homozygous for A and the fetus heterozygous for A/B,and thus reads covering the A allele were shared by the mother and fetus(shared reads).

In one pregnant case analyzed that was used to illustrate several of theconcepts in this invention, the pregnant mother was found to behomozygous at 1,945,516 loci on the autosomes. The maternal plasma DNAsequencing reads that covered these SNPs were inspected. Reads carryinga non-maternal allele was detected at 107,750 loci and these wereconsidered the informative loci. At each informative SNP, the allelethat was not from the mother was termed a fetal-specific allele whilethe other one was termed a shared allele.

A fractional fetal/tumor DNA concentration (also called fetal DNApercentage) in the maternal plasma can be determined. In one embodiment,the fractional fetal DNA concentration in the maternal plasma, f, isdetermined by the equation:

$f = \frac{2p}{p + q}$

where p is the number of sequenced reads with the fetal-specific alleleand q is the number of sequenced reads with the shared allele betweenthe mother and the fetus (Y M D Lo et al. 2010 Sci Transl Med; 2:61ra91). The fetal DNA proportions in the first trimester, thirdtrimester and post-delivery maternal plasma samples were found to be14.4%, 33.9% and 4.5%, respectively. The fetal DNA proportions were alsocalculated using the numbers of reads that were aligned to chromosome Y.Based on the chromosome Y data, the results were 14.2%, 34.9% and 3.7%,respectively, in the first trimester, third trimester and post-deliverymaternal plasma samples.

By separately analyzing the fetal-specific or shared sequence reads,embodiments demonstrate that the circulating fetal DNA molecules weremuch more hypomethylated than the background DNA molecules. Comparisonsof the methylation densities of corresponding loci in the fetal-specificmaternal plasma reads and the placental tissue data for both the firstand third trimesters revealed high levels of correlation. These dataprovided genome level evidence that the placenta is the predominantsource of fetal-derived DNA molecules in maternal plasma and representeda major step forward compared with previous evidence based oninformation derived from selected loci.

We determined the methylation density of each 1-Mb region in the genomeusing either the fetal-specific or shared reads that covered CpG sitesadjacent to the informative SNPs. The fetal and non-fetal-specificmethylomes assembled from the maternal plasma sequence reads can bedisplayed, for example, in Circos plots (M Krzywinski et al. 2009 GenomeRes; 19: 1639-1645). The methylation densities per 1-Mb bin were alsodetermined for the maternal blood cells and placental tissue samples.

FIG. 7A shows a Circos plot 700 for first trimester samples. FIG. 7Bshows a Circos plot 750 for third trimester samples. The plots 700 and750 show methylation density per 1-Mb bin. Chromosome ideograms(outermost ring) are oriented pter-qter in a clockwise direction(centromeres are shown in red). The second outermost track shows thenumber of CpG sites in the corresponding 1-Mb regions. The scale of thered bars shown is up to 20,000 sites per 1-Mb bin. The methylationdensities of the corresponding 1-Mb regions are shown in the othertracks based on the color scheme shown in the center.

For the first trimester samples (FIG. 7A), from inside to outside, thetracks are: chorionic villus sample, fetal-specific reads in maternalplasma, maternal-specific reads in maternal plasma, combined fetal andnon-fetal reads in maternal plasma, and maternal blood cells. For thethird trimester samples (FIG. 7B), the tracks are: term placentaltissue, fetal-specific reads in maternal plasma, maternal-specific readsin maternal plasma, combined fetal and non-fetal reads in maternalplasma, post-delivery maternal plasma and maternal blood cells (from thefirst trimester blood sample). It can be appreciated that for both thefirst and third trimester plasma samples, the fetal methylomes were morehypomethylated than those of the non-fetal-specific methylomes.

The overall methylation profile of the fetal methylomes more closelyresembled that of the CVS or placental tissue samples. On the contrary,the DNA methylation profile of the shared reads in plasma, which werepredominantly maternal DNA, more closely resembled that of the maternalblood cells. We then performed a systematic locus-by-locus comparison ofthe methylation densities of the maternal plasma DNA reads and thematernal or fetal tissues. We determined the methylation densities ofCpG sites that were present on the same sequence read as the informativeSNPs and were covered by at least 5 maternal plasma DNA sequence reads.

FIGS. 8A-8D show plots of comparisons of the methylation densities ofgenomic tissue DNA against maternal plasma DNA for CpG sites surroundingthe informative single nucleotide polymorphisms. FIG. 8A showsmethylation densities for fetal-specific reads in the first trimestermaternal plasma sample relative to methylation densities for reads in aCVS sample. As can be seen, the fetal-specific values correspond well tothe CVS values.

FIG. 8B shows methylation densities for fetal-specific reads in thethird trimester maternal plasma sample relative to methylation densitiesfor reads in a term placental tissue. Again, the sets of densitiescorrespond well, indicating the fetal methylation profile can beobtained by analyzing reads with fetal-specific alleles.

FIG. 8C shows methylation densities for shared reads in the firsttrimester maternal plasma sample relative to methylation densities forreads in maternal blood cells. Given that most of the shared reads arefrom the mother, the two sets of values correspond well. FIG. 8D showsmethylation densities for shared reads in the third trimester maternalplasma sample relative to methylation densities for reads in maternalblood cells.

For the fetal-specific reads in maternal plasma, the Spearmancorrelation coefficient between the first trimester maternal plasma andthe CVS was 0.705 (P<2.2*e-16); and that between the third trimestermaternal plasma and term placental tissue was 0.796 (P<2.2*e-16) (FIGS.8A and 8B). A similar comparison was performed for the shared reads inmaternal plasma with the maternal blood cell data. The Pearsoncorrelation coefficient was 0.653 (P<2.2*e-16) for the first trimesterplasma sample and was 0.638 (P<2.2*e-16) for the third trimester plasmasample (FIGS. 8C and 8D).

B. Fetal Methylome

In one embodiment, to assemble the fetal methylome from maternal plasma,we sorted for sequence reads that spanned at least one informative fetalSNP site and contained at least one CpG site within the same read. Readsthat showed the fetal-specific alleles were included in the assembly ofthe fetal methylome. Reads that showed the shared allele, i.e.non-fetal-specific allele, were included in the assembly of thenon-fetal-specific methylome which was predominantly comprised ofmaternal-derived DNA molecules.

The fetal-specific reads covered 218,010 CpG sites on the autosomes forthe first trimester maternal plasma samples. The corresponding figuresfor the third trimester and post-delivery maternal plasma samples were263,611 and 74,020, respectively. On average, the shared reads coveredthose CpG sites an average of 33.3, 21.7 and 26.3 times, respectively.The fetal-specific reads covered those CpG sites 3.0, 4.4 and 1.8 times,respectively, for the first trimester, third trimester and post-deliverymaternal plasma samples.

Fetal DNA represents a minor population in maternal plasma and thereforethe coverage of those CpG sites by fetal-specific reads was proportionalto the fetal DNA percentage of the sample. For the first trimestermaternal plasma sample, the overall percentage of methylated CpG amongthe fetal reads was 47.0%, while that for the shared reads was 68.1%.For the third trimester maternal plasma sample, the percentage ofmethylated CpG of the fetal reads was 53.3%, while that for the sharedreads was 68.8%. These data showed that the fetal-specific reads inmaternal plasma were more hypomethylated than the shared reads inmaternal plasma

C. Method

The techniques described above can also be used to determine a tumormethylation profile. Methods for determining fetal and tumor methylationprofiles are now described.

FIG. 9 is a flowchart illustrating a method 900 for determining a firstmethylation profile from a biological sample of an organism according toembodiments of the present invention. Method 900 can construct anepigenetic map of the fetus from the methylation profile of maternalplasma. The biological sample includes cell-free DNA comprising amixture of cell-free DNA originating from a first tissue and from asecond tissue. As examples, the first tissue can be from a fetus, atumor, or a transplanted organ.

At block 910, a plurality of DNA molecules are analyzed from thebiological sample. The analysis of a DNA molecule can includedetermining a location of the DNA molecule in a genome of the organism,determining a genotype of the DNA molecule, and determining whether theDNA molecule is methylated at one or more sites.

In one embodiment, the DNA molecules are analyzed using sequence readsof the DNA molecules, where the sequencing is methylation aware. Thus,the sequence reads include methylation status of DNA molecules from thebiological sample. The methylation status can include whether aparticular cytosine residue is 5-methylcytosine or5-hydroxymethylcytosine. The sequence reads can be obtained from varioussequencing techniques, PCR-techniques, arrays, and other suitabletechniques for identifying sequences of fragments. The methylationstatus of sites of the sequence read can be obtained as describedherein.

At block 920, a plurality of first loci are identified at which a firstgenome of the first tissue is heterozygous for a respective first alleleand a respective second allele and a second genome of the second tissueis homozygous for the respective first allele. For example,fetal-specific reads may be identified at the plurality of first loci.Or, tumor-specific reads may be identified at the plurality of firstloci. The tissue-specific reads can be identified from sequencing readswhere the percentage of sequence reads of the second allele fall withina particular range, e.g., about 3%-25%, thereby indicating a minoritypopulation of DNA fragment from a heterozygous genome at the locus and amajority population from a homozygous genome at the locus.

At block 930, DNA molecules located at one or more sites of each of thefirst locus are analyzed. A number of DNA molecules that are methylatedat a site and correspond to the respective second allele of the locusare determined. There may be more than one site per locus. For example,a SNP might indicate that a fragment is fetal-specific, and thatfragment may have multiple sites whose methylation status is determined.The number of reads at each site that are methylated can be determined,and the total number of methylated reads for the locus can bedetermined.

The locus may be defined by a specific number of sites, a specific setof sites, or a particular size for a region around a variation thatcomprises the tissue-specific allele. A locus can have just one site.The sites can have specific properties, e.g., being CpG sites. Thedetermination of a number of reads that are unmethylated is equivalent,and is encompassed within the determination of the methylation status.

At block 940, for each of the first loci, a methylation density iscalculated based on the numbers of DNA molecules methylated at the oneor more sites of the locus and corresponding to the respective secondallele of the locus. For example, a methylation density can bedetermined for CpG sites corresponding to a locus.

At block 950, the first methylation profile of the first tissue iscreated from the methylation densities for the first loci. The firstmethylation profile can correspond to particular sites, e.g., CpG sites.The methylation profile can be for all loci having a fetal-specificallele, or just some of those loci.

IV. Using Difference of Plasma and Blood Methylomes

Above, it was shown that the fetal-specific reads from plasma correlateto the placental methylome. As the maternal component of the maternalplasma methylome is primarily contributed by the blood cells, thedifference between the plasma methylome and blood methylome can be usedto determine the placental methylome for all loci, and not justlocations of fetal-specific alleles. A difference between the plasmamethylome and the blood methylome can also be used to determine amethylome of a tumor.

A. Method

FIG. 10 is a flowchart illustrating a method 1000 of determining a firstmethylation profile from a biological sample of an organism according toembodiments of the present invention. The biological sample (e.g.,plasma) includes cell-free DNA comprising a mixture of cell-free DNAoriginating from a first tissue and from a second tissue. The firstmethylation profile corresponds to a methylation profile of the firsttissue (e.g., fetal tissue or tumor tissue). Method 1200 can provide adeduction of differentially methylated regions from maternal plasma.

At block 1010, a biological sample is received. The biological samplecould simply be received at a machine (e.g., a sequencing machine). Thebiological sample may be in the form taken from the organism or may bein a processed form, e.g., the sample may be plasma that is extractedfrom a blood sample.

At block 1020, a second methylation profile corresponding to DNA of thesecond tissue is obtained. The second methylation profile could be readfrom memory, as it may have been determined previously. The secondmethylation profile can be determined from the second tissue, e.g., adifferent sample that contains only or predominantly cells of the secondtissue. The second methylation profile can correspond to a cellularmethylation profile and be obtained from cellular DNA. As anotherexample, the second profile can be determined from a plasma samplecollected before pregnancy, or before development of cancer because theplasma methylome of a non-pregnant person without cancer is very similarto the methylome of blood cells.

The second methylation profile can provide a methylation density at eachof a plurality of loci in a genome of the organism. The methylationdensity at a particular locus corresponds to a proportion of DNA of thesecond tissue that is methylated. In one embodiment, the methylationdensity is a CpG methylation density, where CpG sites associated withthe locus are used to determine the methylation density. If there is onesite for a locus, then the methylation density can be equal to themethylation index. The methylation density also corresponds to anunmethylation density as the two values are complementary.

In one embodiment, the second methylation profile is obtained byperforming methylation-aware sequencing of cellular DNA from a sample ofthe organism. One example of methylation-aware sequencing includestreating DNA with sodium bisulfite and then performing DNA sequencing.In another example, the methylation-aware sequencing can be performedwithout using sodium bisulfite, using a single molecule sequencingplatform that would allow the methylation status of DNA molecules(including N6-methyladenine, 5-methylcytosine and5-hydroxymethylcytosine) to be elucidated directly without bisulfiteconversion (AB Flusberg et al. 2010 Nat Methods; 7: 461-465; J Shim etal. 2013 Sci Rep; 3:1389. doi: 10.1038/srep01389); or through theimmunoprecipitation of methylated cytosine (e.g. by using an antibodyagainst methylcytosine or by using a methylated DNA binding protein orpeptide (LG Acevedo et al. 2011 Epigenomics; 3: 93-101) followed bysequencing; or through the use of methylation-sensitive restrictionenzymes followed by sequencing. In another embodiment, non-sequencingtechniques are used, such as arrays, digital PCR and mass spectrometry.

In another embodiment, the second methylation density of the secondtissue could be obtained previously from control samples of the subjector from other subjects. The methylation density from another subject canact as a reference methylation profile having reference methylationdensities. The reference methylation densities can be determined frommultiple samples, where a mean level (or other statistical value) of thedifferent methylation densities at a locus can be used as the referencemethylation density at the locus.

At block 1030, a cell-free methylation profile is determined from thecell-free DNA of the mixture. The cell-free methylation profile providesa methylation density at each of the plurality of loci. The cell-freemethylation profile can be determined by receiving sequence reads from asequencing of the cell-free DNA, where the methylation information isobtained with the sequence reads. The cell-free methylation profile canbe determined in a same manner as the cellular methylome.

At block 1040, a percentage of the cell-free DNA from the first tissuein the biological sample is determined. In one embodiment, the firsttissue is fetal tissue, and the corresponding DNA is fetal DNA. Inanother embodiment, the first tissue is tumor tissue, and thecorresponding DNA is tumor DNA. The percentage can be determined in avariety of ways, e.g., using a fetal-specific allele or a tumor-specificallele. Copy number can also be used to determine the percentage, e.g.,as described in U.S. patent application Ser. No. 13/801,748 entitled“Mutational Analysis Of Plasma DNA For Cancer Detection” filed on Mar.13, 3013, which is incorporated by reference.

At block 1050, a plurality of loci for determining the first methylomeare identified. These loci may correspond to each of the loci used todetermine the cell-free methylation profile and the second methylationprofile. Thus, the plurality of loci may correspond. It is possible thatmore loci may be used to determine the cell-free methylation profile andthe second methylation profile.

In some embodiments, loci that were hypermethylated or hypomethylated inthe second methylation profile can be identified, e.g., using maternalblood cells. To identify the loci that were hypermethylated in thematernal blood cells, one can scan from one end of a chromosome for aCpG site with a methylation index ≥X % (e.g., 80%). One can then searchfor the next CpG site within the downstream region (e.g., within 200-bpdownstream). If the immediately downstream CpG site also had amethylation index ≥X % (or other specified amount), the first and thesecond CpG sites can be grouped. The grouping can continue until eitherthere were no other CpG site within the next downstream region; or theimmediately downstream CpG site had a methylation index <X %. The regionof the grouped CpG sites can be reported as hypermethylated in maternalblood cells if the region contained at least five immediately adjacenthypermethylated CpG sites. A similar analysis can be performed to searchfor loci that were hypomethylated in maternal blood cells for CpG siteswith methylation indices ≤20%. The methylation densities for the secondmethylation profile can be calculated for the short-listed loci and usedto deduce the first methylation profile (e.g., placental tissuemethylation density) of the corresponding loci, e.g., from maternalplasma bisulfate-sequencing data.

At block 1060, the first methylation profile of the first tissue isdetermined by calculating a differential parameter that includes adifference between the methylation density of the second methylationprofile and the methylation density of the cell-free methylation profilefor each of the plurality of loci. The difference is scaled by thepercentage.

In one embodiment, the first methylation density of a locus in the first(e.g., placental) tissue (D) was deduced using the equation:

$\begin{matrix}{D = {{mbc} - \frac{\left( {{mbc} - {mp}} \right)}{f*CN}}} & (1)\end{matrix}$

where mbc denotes the methylation density of the second methylationprofile at a locus (e.g., a short-listed locus as determined in thematernal blood cell bisulfate-sequencing data); mp denotes themethylation density of the corresponding locus in the maternal plasmabisulfate-sequencing data; f represented the percentage of cell-free DNAfrom the first tissue (e.g., fractional fetal DNA concentration), and CNrepresents copy number at the locus (e.g., a higher value foramplifications or a lower number for deletions relative to normal). Ifthere is no amplification or deletion in the first tissue then CN can beone. For trisomy (or a duplication of the region in a tumor or a fetus),CN would be 1.5 (as the increase is from 2 copies to 3 copies) andmonosomy would have 0.5. Higher amplification can increase by incrementsof 0.5. In this example, D can correspond to the differential parameter.

At block 1070, the first methylation density is transformed to obtain acorrected first methylation density of the first tissue. Thetransformation can account for fixed differences between thedifferential parameters and the actual methylation profile of the firsttissue. For example, the values may differ by a fixed constant or by aslope. The transformation can be linear or non-linear.

In one embodiment, the distribution of the deduced values, D, was foundto be lower than the actual methylation level of the placental tissue.For example, the deduced values can be linearly transformed using datafrom CpG islands, which were genomic segments that had anoverrepresentation of CpG sites. The genomic positions of CpG islandsused in this study were obtained from the UCSC Genome Browser database(NCBI build 36/hg18) (PA Fujita et al. 2011 Nucleic Acids Res; 39:D876-882). For example, a CpG island can be defined as a genomic segmentwith GC content ≥50%, genomic length >200 bp and the ratio ofobserved/expected CpG number >0.6 (M Gardiner-Garden et al 1987 J MolBiol; 196: 261-282).

In one implementation, to derive the linear transformation equation, CpGislands with at least 4 CpG sites and an average read depth ≥5 per CpGsite in the sequenced samples can be included. After determining thelinear relationships between the methylation densities of CpG islands inthe CVS or term placenta and the deduced values, D, the followingequations were used to determine the predicted values:

First trimester predicted values=D×1.6+0.2

Third trimester predicted values=D×1.2+0.05

B. Fetal Example

As mentioned above, method 1000 can be used to deduce a methylationlandscape of the placenta from maternal plasma. Circulating DNA inplasma is predominately originated from hematopoietic cells. Still thereis an unknown proportion of cell-free DNA contributed from otherinternal organs. Moreover, placenta-derived cell-free DNA accounts forapproximately 5-40% of the total DNA in maternal plasma, with a mean ofapproximately 15%. Thus, one can make an assumption that the methylationlevel in maternal plasma is equivalent to an existing backgroundmethylation plus a placental contribution during pregnancy, as describedabove.

The maternal plasma methylation level, MP, can be determined using thefollowing equation:

MP=BKG×(1−f)+PLN×f

where BKG is the background DNA methylation level in plasma derived fromblood cells and internal organs, PLN is the methylation level ofplacenta and f is the fractional fetal DNA concentration in maternalplasma.

In one embodiment, the methylation level of placenta can theoreticallybe deduced by:

$\begin{matrix}{{PLN} = \frac{{MP} - {{BKG} \times \left( {1 - f} \right)}}{f}} & (2)\end{matrix}$

Equations (1) and (2) are equivalent when CN equals one, D equals PLN,and BKG equals mbc. In another embodiment, the fractional fetal DNAconcentration can be assumed or set to a specified value, e.g., as partof an assumption of a minimum f being present.

The methylation level of maternal blood was taken to represent thebackground methylation of maternal plasma. Besides the loci that werehypermethylated or hypomethylated in maternal blood cells, we furtherexplored the deduction approach by focusing on defined regions withclinical relevance, for instance, CpG islands in the human genome.

The mean methylation density of a total of 27,458 CpG islands (NCBIBuild36/hg18) on the autosomes and chrX was derived from the sequencingdata of maternal plasma and placenta. Only those with ≥10 CpG sitescovered and an average read depth ≥5 per covered CpG sites in allanalyzed samples, including the placenta, maternal blood and maternalplasma, were selected. As a result, 26,698 CpG islands (97.2%) remainedas valid and their methylation level was deduced using the plasmamethylation data and the fractional fetal DNA concentration according tothe above equation.

It was noticed that the distribution of deduced PLN values was lowerthan the actual methylation level of CpG islands in the placentaltissue. Thus, in one embodiment, the deduced PLN values, or simplydeduced values (D), were used as an arbitrary unit for estimating themethylation level of CpG islands in the placenta. After atransformation, the deduced values linearly and their distributionbecame more alike to the actual dataset. The transformed deduced valueswere named methylation predictive values (MPV) and subsequently used forpredicting the methylation level of genetic loci in the placenta.

In this example, the CpG islands were classified into 3 categories basedon their methylation densities in the placenta: Low (≤0.4), Intermediate(>0.4-<0.8) and High (≥0.8). Using the deduction equation, we calculatedthe MPV of the same set of CpG islands and then used the values toclassify them into 3 categories with the same cutoffs. By comparing theactual and the deduced datasets, we found that 75.1% of the short-listedCpG islands could be matched correctly to the same categories in thetissue data according to their MPV. About 22% of the CpG islands wereassigned to groups with 1-level difference (high versus intermediate, orintermediate versus low) and less than 3% would be completelymisclassified (high versus low) (FIG. 12A). The overall classificationperformance was also determined: 86.1%, 31.4% and 68.8% of CpG islandswith methylation densities ≤0.4, >0.4-<0.8 and ≥0.8 in the placenta werededuced to be “Low”, “Intermediate” and “High” correctly (FIG. 12B).

FIGS. 11A and 11B show graphs of the performance of the predictingalgorithm using maternal plasma data and fractional fetal DNAconcentration according to embodiments of the present invention. FIG.11A is a graph 1100 showing the accuracy of CpG island classificationusing the MPV correction classification (the deduced category matchesexactly the actual dataset); 1-level difference (the deduced category is1-level different from the actual dataset); and misclassification (thededuced category is opposite to the actual dataset). FIG. 11B is a graph1150 showing the proportion of CpG islands classified in each deducedcategory.

Provided that the maternal background methylation is low in therespective genomic regions, the presence of hypermethylatedplacental-derived DNA in the circulation would increase the overallplasma methylation level to a degree depending on the fractional fetalDNA concentration. A marked change could be observed when the fetal DNAreleased is fully methylated. On the contrary, when the maternalbackground methylation is high, the degree of change in the plasmamethylation level would become more significant if hypomethylated fetalDNA is released. Therefore, the deduction scheme may be more practicalwhen the methylation level was deduced for genetic loci which are knownto be distinct between the maternal background and the placenta,especially for those hypermethylated and hypomethylated markers in theplacenta.

FIG. 12A is a table 1200 showing details of 15 selected genomic loci formethylation prediction according to embodiments of the presentinvention. To confirm techniques, we selected 15 differentiallymethylated genomic loci which had been studied previously. Themethylation levels of selected regions were deduced and compared to thepreviously studied 15 differentially methylated genetic loci (R W K Chiuet al. 2007 Am J Pathol; 170: 941-950; S. S. C. Chim et al. 2008 ClinChem; 54: 500-511; S S C Chim et al. 2005 Proc Natl Acad Sci USA; 102:14753-14758; D W Y Tsui et al. 2010 PLoS One; 5: e15069).

FIG. 12B is a graph 1250 showing the deduced categories of the 15selected genomic loci and their corresponding methylation levels in theplacenta. Deduced methylation categories are: Low, ≤0.4;Intermediate, >0.4-<0.8; High, ≥0.8. Table 1200 and graph 1300 show thattheir methylation levels in the placenta could be deduced correctly withseveral exceptions: RASSF1A, CGI009, CGI137 and VAPA. Out of these 4markers, only CGI009 showed a marked discrepancy with the actualdataset. The others were just marginally misclassified.

In table 1200, “1” refers to the deduced values (D) being calculated bythe equation:

$D = \frac{{MP} - {{BKG} \times \left( {1 - f} \right)}}{f}$

where f is the fraction fetal DNA concentration. The label “2” refers tothe methylation predictive values (MPV) referring to the linearlytransformed deduced values using the equation: MPV=D×1.6+0.25. Label “3”refers to the classification cutoff for the deduced values: Low, ≤0.4;Inter(mediate), >0.4-<0.8; High, ≥0.8. Label “4” refers to theclassification cutoff for the actual placental dataset: Low, ≤0.4;Inter(mediate), >0.4-<0.8; High, ≥0.8. Label “5” denotes that placentalstatus refers to the methylation status of placenta relative to that ofmaternal blood cells.

C. Calculation of Fractional Concentrations of Fetal DNA

In one embodiment, the percentage of fetal DNA from the first tissue canuse a Y chromosome for a male fetus. The proportion of chromosome Y (%chrY) sequences in a maternal plasma sample was a composite of thechromosome Y reads derived from the male fetus and the number ofmaternal (female) reads that were misaligned to chromosome Y (R W K Chiuet al. 2011 BMJ; 342: c7401). Thus, the relationship between % chrY andthe fractional fetal DNA concentration (f) in the sample can be givenby:

% chrY=% chrY _(male) ×f+% chrY _(female)×(1−f)

where % chrY_(male) refers to a proportion of reads aligned tochromosome Y in a plasma sample containing 100% male DNA; and %chrY_(female) refers to the proportion of reads aligned to chromosome Yin a plasma sample containing 100% female DNA.

% chrY can be determined from reads that were aligned to chromosome Ywith no mismatches for a sample from a female pregnant with a malefetus, e.g., where the reads are from bisulfite-converted samples. The %chrY_(male) value can be obtained from the bisulfite-sequencing of twoadult male plasma samples. The % chrY_(female) value can be obtainedfrom the bisulfate-sequencing of two non-pregnant adult female plasmasamples.

In other embodiments, the fetal DNA percentage can be determined fromfetal-specific alleles on an autosome. As another example, epigeneticmarkers may be used to determine the fetal DNA percentage. Other ways ofdetermining the fetal DNA percentage may also be used.

D. Method of Using Methylation to Determine Copy Number

The placental genome is more hypomethylated than the maternal genome. Asdiscussed above the methylation of the plasma of a pregnant woman isdependent on the fractional concentration of placentally-derived fetalDNA in the maternal plasma. Therefore, through the analysis of themethylation density of a chromosomal region, it is possible to detectthe difference in the contribution of fetal tissues to the maternalplasma. For example, in a pregnant woman carrying a trisomic fetus (e.g.suffering from trisomy 21 or trisomy 18 or trisomy 13), the fetus wouldcontribute an additional amount of the DNA from the trisomic chromosometo the maternal plasma when compared with the disomic chromosomes. Inthis situation, the plasma methylation density for the trisomicchromosome (or any chromosomal region that has an amplification) wouldbe lower than those for the disomic chromosomes. The degree ofdifference can be predicted by mathematical calculation by taking intoaccount the fractional fetal DNA concentration in the plasma sample. Thehigher the fractional fetal DNA concentration in the plasma sample thelarger the difference in methylation density between the trisomic anddisomic chromosomes would be. For regions having a deletion, themethylation density would be higher.

One example of a deletion is Turner syndrome, when a female fetus wouldhave only one copy of chromosome X. In this situation, for a pregnantwoman carrying a fetus suffering from Turner syndrome, the methylationdensity of chromosome X in her plasma DNA would be higher than thesituation of the same pregnant woman carrying a female fetus having thenormal number of chromosome X. In one embodiment of this strategy, onecould first analyze maternal plasma for the presence or absence ofchromosome Y sequences (e.g. using MPS or a PCR-based technique). Ifchromosome Y sequences are present, then the fetus can be classified asmale and the following analysis would not be necessary. On the otherhand, if chromosome Y sequences are absent in maternal plasma, then thefetus can be classified as female. In this situation, one can thenanalyze the methylation density of chromosome X in maternal plasma. Ahigher chromosome X methylation density than normal would indicate thatthe fetus has a high risk of having Turner syndrome. This approach canalso be applied for the other sex chromosomal aneuploidies. For example,for a fetus affected by XYY, the methylation density for the Ychromosome in maternal plasma would be lower than that normal XY fetushaving a similar level of fetal DNA in maternal plasma. As anotherexample, for a fetus suffering from Klinefelter syndrome (XXY),chromosome Y sequences are present in maternal plasma, but themethylation density of chromosome X in maternal plasma will be lowerthan that of a normal XY fetus having a similar level of fetal DNA inmaternal plasma.

From the previous discussion, the plasma methylation density for adisomic chromosome (MP_(Non-aneu)) can be calculated as:MP_(Non-aneu)=BKG×(1−f)+PLN×f, where BKG is the background DNAmethylation level in plasma derived from blood cells and internalorgans, PLN is the methylation level of placenta and f is the fractionalfetal DNA concentration in maternal plasma.

The plasma methylation density for a trisomic chromosome (MP_(Aneu)) canbe calculated as: MP_(Aneu)=BKG×(1−f)+PLN×f×1.5, where the 1.5corresponds to the copy number CN and the addition of one morechromosome is a 50% increase. The difference between a trisomic anddisomic chromosomes (MP_(Diff)) would be

MP_(Diff) =PLN×f×0.5.

In one embodiment, a comparison of the methylation density of thepotentially aneuploid chromosome (or chromosomal region) to one or moreother presumed non-aneuploid chromosome(s) or the overall methylationdensity of the genome can be used to effectively normalize the fetal DNAconcentration in the plasma sample. The comparison can be via acalculation of a parameter (e.g., involving a ratio or a difference)between the methylation densities of the two regions to obtain anormalized methylation density. The comparison can remove a dependenceof the resulting methylation level (e.g., determined as a parameter fromthe two methylation densities).

If the methylation density of the potentially aneuploid chromosome isnot normalized to the methylation density of one or more otherchromosome(s), or other parameters that reflect the fractionalconcentration of fetal DNA, the fractional concentration would be amajor factor affecting the methylation density in the plasma. Forexample, the plasma methylation density of chromosome 21 of a pregnantwoman carrying a trisomy 21 fetus with a fractional fetal DNAconcentration of 10% would be the same as that of a pregnant womancarrying a euploid fetus and the fractional fetal DNA concentration is15%, whereas a normalized methylation density would show a difference.

In another embodiment, the methylation density of the potentiallyaneuploid chromosome can be normalized to the fractional fetal DNAconcentration. For example, the following equation can be applied tonormalize the methylation density:MP_(Normalized)=MP_(non-normalized)+(BKG−PLN)×f, where MP_(Normalized)is the methylation density normalized with the fractional fetal DNAconcentration in the plasma, MP_(non-normalized) is the measuredmethylation density, BKG is the background methylation density frommaternal blood cells or tissues, PLN is the methylation density in theplacental tissues, and f is the fractional fetal DNA concentration. Themethylation densities of BKG and PLN could be based on reference valuespreviously established from maternal blood cells and placental tissuesobtained from healthy pregnancies. Different genetic and epigeneticmethods can be used for the determination of the fractional fetal DNAconcentration in the plasma sample, for example by the measurement ofthe percentage of sequence reads from the chromosome Y using massivelyparallel sequencing or PCR on non-bisulfite-converted DNA.

In one implementation, the normalized methylation density for apotentially aneuploid chromosome can be compared to a reference groupwhich consists of pregnant woman carrying euploid fetuses. The mean andSD of the normalized methylation density of the reference group can bedetermined. Then the normalized methylation density of the tested casecan be expressed as a z-score which indicates the number of SDs from themean of the reference group by:

${{z - {score}} = \frac{{MP_{Normalized}} - {Mean}}{SD}},$

where MP_(Normalized) is the normalized methylation density for thetested case, Mean is the mean of the normalized methylation density ofthe reference cases and SD is the standard deviation of the normalizedmethylation density of the reference cases. A cutoff, for examplez-score <−3, can be used to classify if a chromosome is significantlyhypomethylated and, hence, to determine the aneuploidy status of thesample.

In another embodiment, the MP_(Diff) can be used as the normalizedmethylation density. In such an embodiment, PLN can be deduced, e.g.,using method 1000. In some implementations, a reference methylationdensity (which can be normalized using f) can be determined from amethylation level of a non-aneuploid region. For example, the Mean couldbe determined from one or more chromosomal regions of the same sample.The cutoff could be scaled by f, or just set to a level sufficient aslong as a minimum concentration exists.

Accordingly, a comparison of a methylation level for a region to acutoff can be accomplished in various ways. The comparison can involve anormalization (e.g., as described above), which may be performedequivalently on the methylation level or the cutoff value, depending onhow the values are defined. Thus, whether the determined methylationlevel of a region is statistically different than a reference level(determined from same sample or other samples) can be determined in avariety of ways.

The above analysis can be applied to the analysis of chromosomalregions, which can include a whole chromosome or parts of thechromosome, including contiguous or disjoint subregions of a chromosome.In one embodiment, the potentially aneuploid chromosome can be dividedinto a number of bins. The bins can be of the same or different sizes.The methylation density of each bin can be normalized to the fractionalconcentration of the sample or to the methylation density of one or morepresumed non-aneuploid chromosome(s) or the overall methylation densityof the genome. The normalized methylation density of each bin can thenbe compared with a reference group to determine if it is significantlyhypomethylated. Then the percentage of bins being significantlyhypomethylated can be determined. A cutoff, for examples more than 5%,10%, 15%, 20% or 30% of the bins being significantly hypomethylated canbe used to classify the aneuploidy status of the case.

When one is testing for an amplification or a deletion, one can comparethe methylation density to a reference methylation density, which may bespecific for a particular region being tested. Each region may have adifferent reference methylation density as methylation can vary fromregion to region, particularly depending on the size of the regions(e.g., smaller regions will show more variation).

As mentioned above, one or more pregnant women each carrying a euploidfetus can be used to define the normal range of the methylation densityfor a region of interest or a difference in methylation density betweentwo chromosomal regions. A normal range can also be determined for thePLN (e.g., by direct measurement or as deduced by method 1000). In otherembodiments, a ratio between two methylation densities can be used,e.g., of a potentially aneuploid chromosome and a non-aneuploidchromosome can be used for the analysis instead of their difference.This methylation analysis approach can be combined with sequence readcounting approach (R W K Chiu et al. 2008 Proc Natl Acad Sci USA;105:20458-20463) and approaches involving size analysis of plasma DNA(US patent 2011/0276277) to determine or confirm an aneuploidy. Thesequence read counting approach that is used in combination withmethylation analysis can be performed either using random sequencing (RW K Chiu et al. 2008 Proc Natl Acad Sci USA; 105:20458-20463; D WBianchi D W et al. 2012 Obstet Gynecol 119:890-901) or targetedsequencing (AB Sparks et al. 2012 Am J Obstet Gynecol 206:319.e1-9; BZimmermann et al. 2012 Prenat Diagn 32:1233-1241; G J Liao et al. 2012PLoS One; 7:e38154).

The use of BKG can account for variations in the background betweensamples. For example, one female might have different BKG methylationlevels than another female, but a difference between the BKG and PLN canbe used across samples in such situations. The cutoff for differentchromosomal regions can be different, e.g., when a methylation densityof one region of the genome differs relative to another region of thegenome.

This approach can be generalized to detect any chromosomal aberrations,including deletion and amplification, in the fetal genome. In addition,the resolution of this analysis can be adjusted to the desired level,for example, the genome can be divided into 10 Mb, 5 Mb, 2 Mb, 1 Mb, 500kb, 100 kb bins. Hence, this technology can also be used for detectingsubchromosomal duplication or subchromosomal deletion. This technologywould thus allow a prenatal fetal molecular karyotype to be obtainednoninvasively. When used in this manner, this technology can be used incombination with the noninvasive prenatal testing methods that are basedon the counting of molecules (A Srinivasan et al. 2013 Am J Hum Genet;92:167-176; S C Y Yu et al. 2013 PLoS One 8: e60968). In otherembodiments, the size of the bins need not be identical. For example,the size of the bins may be adjusted so that each bin contains anidentical number of CpG dinucleotides. In this case, the physical sizeof the bins would be different.

The equation can be rewritten to apply to different types of chromosomeaberrations as MP_(Diff)=(BKG−PLN)×f×0.5×CN. Here CN represents thenumber of copy number change at the affected region. CN equals to 1 forthe gain of 1 copy of a chromosome, 2 for the gain of 2 copies of achromosome and −1 for the loss of one of the two homologous chromosomes(e.g. for detecting fetal Turner syndrome in which a female fetus haslost one of the X chromosomes, leading to a XO karyotype). This equationneed not be changed when the size of the bins are changed. However, thesensitivity and specificity may reduce when smaller bin size is usedbecause a smaller number of CpG dinucleotides (or other nucleotidecombinations showing differential methylation between fetal DNA andmaternal DNA) would be present in smaller bins, leading to increasedstochastic variation in the measurement of methylation densities. In oneembodiment, the number of reads required can be determined by analyzingthe coefficient of variation of the methylation density and the desiredlevel of sensitivity.

To demonstrate the feasibility of this approach, we have analyzed theplasma samples from 9 pregnant women. In five pregnant women, each wascarrying a euploid fetus and the other four were each carrying a trisomy21 (T21) fetus. Three of the five euploid pregnancies were randomlyselected to form a reference group. The remaining two euploid pregnancycases (Eu1 and Eu2) and the four T21 cases (T21-1, T21-2, T21-3 andT21-4) were analyzed using this approach to test for a potential T21status. The plasma DNA was bisulfate-converted and sequenced using theIllumina HiSeq2000 platform. In one embodiment, the methylation densityof individual chromosomes were calculated. The difference in methylationdensity between chromosome 21 and the mean of the other 21 autosomes wasthen determined to obtain a normalized methylation density (Table 1).The mean and SD of the reference group was used for the calculation ofthe z-score of the six test cases.

TABLE 1 Using a cutoff of <−3 for z-score to classify a sample to beT21, the classification of all the euploid and T21 cases were correct.Eu1 Eu2 T21-1 T21-2 T21-3 T21-4 z-score for MP_(Diff) −1.48 1.09 −4.46−5.30 −8.06 −5.69 between chr 21 and other autosomes

In another embodiment, the genome was divided into 1 Mb bins and themethylation density for each 1 Mb bin was determined. The methylationdensity of all the bins on the potentially aneuploid chromosome can benormalized with the median methylation density of all the bins locatedon the presumed non-aneuploid chromosomes. In one implementation, foreach bin, the difference in methylation density from the median of thenon-aneuploid bins can be calculated. The z-score can be calculated forthese values using the mean and SD values of the reference group. Thepercentage of bins showing hypomethylation (Table 2) can be determinedand compared to a cutoff percentage.

TABLE 2 Using 5% as a cutoff for the bins with significantly morehypomethylated on chromosome 21, all the cases were classified correctlyfor T21 status. Eu1 Eu2 T21-1 T21-2 T21-3 T21-4 Percentage of 0% 0%33.3% 58.3% 19.4% 52.8% bins on chr 21 have a z-score of MP_(Diff) < −3

This DNA methylation-based approach for detecting fetal chromosomal orsubchromosomal aberrations can be used in conjunction with those basedon the counting of molecules such as by sequencing (R W K Chiu et al.2008 Proc Natl Acad Sci USA; 105: 20458-20463) or digital PCR (Y M D Loet al. 2007 Proc Natl Acad Sci USA; 104: 13116-13121), or the sizing ofDNA molecules (US Patent Publication 2011/0276277). Such combination(e.g. DNA methylation plus molecular counting, or DNA methylation plussizing, or DNA methylation plus molecular counting plus sizing) wouldhave a synergistic effect which would be advantageous in a clinicalsetting, e.g. improving the sensitivity and/or specificity. For example,the number of DNA molecules that would need to be analyzed, e.g. bysequencing, can be reduced without adversely impacting the diagnosticaccuracy. This feature would allow such tests to be done moreeconomically. As another example, for a given number of DNA moleculesanalyzed, a combined approach would allow fetal chromosomal orsubchromosomal aberrations to be detected at a lower fractionalconcentration of fetal DNA.

FIG. 13 is a flowchart of a method 1300 for detecting a chromosomalabnormality from a biological sample of an organism. The biologicalsample includes cell-free DNA comprising a mixture of cell-free DNAoriginating from a first tissue and from a second tissue. The firsttissue may be from a fetus or tumor and the second tissue may be from apregnant female or a patient.

At block 1310, a plurality of DNA molecules from the biological sampleare analyzed. The analysis of a DNA molecule can include determining alocation of the DNA molecule in a genome of the organism and determiningwhether the DNA molecule is methylated at one or more sites. Theanalysis can be performed by receiving sequence reads from amethylation-aware sequencing, and thus the analysis can be performedjust on data previously obtained from the DNA. In other embodiments, theanalysis can include the actual sequencing or other active steps ofobtaining the data.

The determining of a location can include mapping the DNA molecules(e.g., via sequence reads) to respective parts of the human genome,e.g., to specific regions. In one implementation, if a read does not mapto a region of interest, then the read can be ignored.

At block 1320, a respective number of DNA molecules that are methylatedat the site is determined for each of a plurality of sites. In oneembodiment, the sites are CpG sites, and may be only certain CpG sites,as selected using one or more criteria mentioned herein. The number ofDNA that are methylated is equivalent to determining the number that areunmethylated once normalization is performed using a total number of DNAmolecules analyzed at a particular site, e.g., a total number ofsequence reads.

At block 1330, a first methylation level of a first chromosomal regionis calculated based on the respective numbers of DNA moleculesmethylated at sites within the first chromosomal region. The firstchromosomal region can be of any size, e.g., sizes mentioned above. Themethylation level can account for a total number of DNA moleculesaligned to the first chromosomal region, e.g., as part of anormalization procedure.

The first chromosomal region may be of any size (e.g., a wholechromosome) and may be composed of disjointed subregions, i.e.,subregions are separated from each other. Methylation levels of eachsubregion can be determined and the combined, e.g., as an average ormedian, to determine a methylation level for the first chromosomalregion.

At block 1340, the first methylation level is compared to a cutoffvalue. The cutoff value may be a reference methylation level or berelated to a reference methylation level (e.g., a specified distancefrom a normal level). The cutoff value may be determined from otherfemale pregnant subjects carrying fetuses without a chromosomalabnormality for the first chromosomal region, from samples ofindividuals without cancer, or from loci of the organism that are knownto not be associated with an aneuploidy (i.e., regions that aredisomic).

In one embodiment, the cutoff value can be defined as having adifference from a reference methylation level of (BKG−PLN)×f×0.5×CN,where BKG is the background of the female (or an average or median fromother subjects), f is the fractional concentration of cell-free DNAoriginating from the first tissue, and CN is a copy number being tested.CN is an example of a scale factor corresponding to a type ofabnormality (deletion or duplication). A cutoff for a CN of 1 can beused to test all amplifications initially, and then further cutoffs canbe used to determine the degree of amplification. The cutoff value canbe based on a fractional concentration of cell-free DNA originating fromthe first tissue to determine the expected level of methylation for alocus, e.g., if no copy number aberration is present.

At block 1350, a classification of an abnormality for the firstchromosomal region is determined based on the comparison. Astatistically significant difference in levels can indicate increasedrisk of the fetus having a chromosomal abnormality. In variousembodiments, the chromosomal abnormality can be trisomy 21, trisomy 18,trisomy 13, Turner syndrome, or Klinefelter syndrome. Other examples area subchromosomal deletion, subchromosomal duplication, or DiGeorgesyndrome.

V. Determination of Markers

As noted above, certain parts of the fetal genome are methylateddifferently than the maternal genome. These differences can be commonacross pregnancies. The regions of different methylation can be used toidentify DNA fragments that are from the fetus.

A. Method to Determine DMRs from Placental Tissue and Maternal Tissue

The placenta has tissue-specific methylation signatures. Fetal-specificDNA methylation markers have been developed for maternal plasmadetection and for noninvasive prenatal diagnostic applications based onloci that are differentially methylated between placental tissues andmaternal blood cells (S S C Chim et al. 2008 Clin Chem; 54: 500-511; EAPapageorgiou et al 2009 Am J Pathol; 174: 1609-1618; and T Chu et al.2011 PLoS One; 6: e14723). Embodiments for mining for suchdifferentially methylated regions (DMRs) on a genome-wide basis areprovided.

FIG. 14 is a flowchart of a method 1400 for identifying methylationmarkers by comparing a placental methylation profile to a maternalmethylation profile (e.g., determined from blood cells) according toembodiments of the present invention. Method 1400 may also be used todetermine markers for a tumor by comparing a tumor methylation profileto a methylation profile corresponding to healthy tissue.

At block 1410, a placental methylome and a blood methylome is obtained.The placental methylome can be determined from a placental sample, e.g.,CVS or a term placenta. Methylome should be understood to possibleinclude methylation densities of only part of a genome.

At block 1420, a region is identified that includes a specified numberof sites (e.g., 5 CpG sites) and for which a sufficient number of readshave been obtained. In one embodiment, the identification began from oneend of each chromosome to locate the first 500-bp region that containedat least five qualified CpG sites. A CpG site may be deemed qualified ifthe site was covered by at least five sequence reads.

At block 1430, a placental methylation index and a blood methylationindex is calculated for each site. For example, the methylation indexwas calculated individually for all qualified CpG sites within each500-bp region.

At block 1440, the methylation indices were compared between thematernal blood cells and the placental sample to determine if the setsof indices were different between each other. For example, themethylation indices were compared between the maternal blood cells andthe CVS or the term placenta using, for example, the Mann-Whitney test.A P-value of, for example, ≤0.01 was considered as statisticallysignificantly different, although other values may be used, where alower number would reduce false positive regions.

In one embodiment, if the number of qualified CpG sites was less thanfive or the Mann-Whitney test was non-significant, the 500-bp regionshifted downstream for 100 bp. The region continued to be shifteddownstream until the Mann-Whitney test became significant for a 500-bpregion. The next 500-bp region would then be considered. If the nextregion was found to exhibit statistical significance by the Mann-Whitneytest, it would be added to the current region as long as the combinedcontiguous region is no larger than 1,000 bp.

At block 1450, adjacent regions that were statistically significantlydifferent (e.g., by the Mann-Whitney test) can be merged. Note thedifference is between the methylation indices for the two samples. Inone embodiment, if the adjacent regions are within a specified distance(e.g., 1,000 bp) of each other and if they showed a similar methylationprofile then they would be merged. In one implementation, the similarityof the methylation profile between adjacent regions can be defined usingany of the following: (1) showing the same trend in the placental tissuewith reference to the maternal blood cells, e.g. both regions were moremethylated in the placental tissues than the blood cells; (2) withdifferences in methylation densities of less than 10% for the adjacentregions in the placental tissue; and (3) with differences in methylationdensities of less than 10% for the adjacent regions in the maternalblood cells.

At block 1460, methylation densities of the blood methylome frommaternal blood cell DNA and placental sample (e.g., CVS or termplacental tissue) at the regions were calculated. The methylationdensities can be determined as described herein.

At block 1470, putative DMRs where total placental methylation densityand a total blood methylation density for all the sites in the regionare statistically significantly different is determined. In oneembodiment, all qualified CpG sites within a merged region are subjectedto a χ² test. The χ² test assessed if the number of methylated cytosinesas a proportion of the methylated and unmethylated cytosines among allthe qualified CpG sites within the merged region was statisticallysignificantly different between the maternal blood cells and placentaltissue. In one implementation, for the χ² test, a P-value of ≤0.01 maybe considered as statistically significantly different. The mergedsegments that showed significance by the χ² test were considered asputative DMRs.

At block 1480, loci where the methylation densities of the maternalblood cell DNA were above a high cutoff or below a low cutoff wereidentified. In one embodiment, loci were identified where themethylation densities of the maternal blood cell DNA were either ≤20% or≥80%. In other embodiments, bodily fluids other than maternal blood canbe used, including, but not limited to saliva, uterine or cervicallavage fluid from the female genital tract, tears, sweat, saliva, andurine.

A key to the successful development of DNA methylation markers that arefetal-specific in maternal plasma can be that the methylation status ofthe maternal blood cells are either as highly methylated or asunmethylated as possible. This can reduce (e.g., minimize) the chance ofhaving maternal DNA molecules interfering with the analysis of theplacenta-derived fetal DNA molecules which show an opposite methylationprofile. Thus, in one embodiment, candidate DMRs were selected byfurther filtering. The candidate hypomethylated loci were those thatshowed methylation densities ≤20% in the maternal blood cells and withat least 20% higher methylation densities in the placental tissues. Thecandidate hypermethylated loci were those that showed methylationdensities ≥80% in the maternal blood cells and with at least 20% lowermethylation densities in the placental tissues. Other percentages may beused.

At block 1490, DMRs were then identified among the subset of loci wherethe placental methylation densities are significantly different from theblood methylation densities by comparing the difference to a threshold.In one embodiment, the threshold is 20%, so the methylation densitiesdiffered by at least 20% from the methylation densities of the maternalblood cells. Accordingly, a difference between placental methylationdensities and blood methylation densities at each identified loci can becalculated. The difference can be a simple subtraction. In otherembodiments, scaling factors and other functions can be used todetermine the difference (e.g., the difference can be the result of afunction applied to the simple subtraction).

In one implementation, using this method, 11,729 hypermethylated and239,747 hypomethylated loci were identified from the first trimesterplacental sample. The top 100 hypermethylated loci are listed in tableS2A of the appendix of U.S. application Ser. No. 13/842,209. The top 100hypomethylated loci are listed in table S2B of the appendix of U.S.application Ser. No. 13/842,209. The tables S2A and S2B list thechromosome, the start and end location, the size of the region, themethylation density in maternal blood, the methylation density in theplacenta sample, the P-values (which are all very small), and themethylation difference. The locations correspond to reference genomehg18, which can be found athgdownload.soe.ucsc.edu/goldenPath/hg18/chromosomes.

11,920 hypermethylated and 204,768 hypomethylated loci were identifiedfrom the third trimester placental sample. The top 100 hypermethylatedloci for the 3^(rd) trimester are listed in table S2C, and the top 100hypomethylated loci are listed in table S2D. Thirty-three loci that werepreviously reported to be differentially methylated between maternalblood cells and first trimester placental tissues were used to validateour list of first trimester candidates. 79% of the 33 loci had beenidentified as DMRs using our algorithm.

FIG. 15A is a table 1500 showing a performance of DMR identificationalgorithm using first trimester data with reference to 33 previouslyreported first trimester markers. In the table, “a” indicates that loci1 to 15 were previously described in (R W K Chiu et al. 2007 Am JPathol; 170:941-950 and S S C Chim et al. 2008 Clin Chem; 54:500-511);loci 16 to 23 were previously described in (KC Yuen, thesis 2007, TheChinese University of Hong Kong, Hong Kong); and loci 24 to 33 werepreviously described in (EA Papageorgiou et al. 2009 Am J Pathol;174:1609-1618). “b” indicates that these data were derived from theabove publications. “c” indicates that methylation densities of maternalblood cells and chorionic villus sample and their differences wereobserved from the sequencing data generated in the present study butbased on the genomic coordinates provided by the original studies. “d”indicates that data on the loci identified using embodiments of method1400 on the bisulfite sequencing data without taking reference from thepublications cited above by Chiu et al (2007), Chim et al (2008), Yuen(2007) and Papageorgiou et al (2009). The span of the loci included thepreviously reported genomic regions but in general spanned largerregions. “e” indicates that a candidate DMR was classified astrue-positive (TP) or false-negative (FN) based on the requirement ofobserving >0.20 difference between the methylation densities of thecorresponding genome coordinates of the DMRs in maternal blood cells andchorionic villus sample.

FIG. 15B is a table 1550 showing a performance of DMR identificationalgorithm using third trimester data and compared with the placentasample obtained at delivery. “a” indicates that the same list of 33 locias described in FIG. 17A were used. “b” indicates that as the 33 lociwere previously identified from early pregnancy samples, they might notbe applicable to the third trimester data. Hence, the bisulfitesequencing data generated in the present study on the term placentaltissue based on the genomic coordinates provided by the original studieswere reviewed. A difference of >0.20 in the methylation densitiesbetween the maternal blood cell and term placental tissue was used todetermine if the loci were indeed true DMRs in the third trimester. “c”indicates that the data on the loci was identified using method 1400 onthe bisulfate sequencing data without taking reference from previouslycited publications by Chiu et al (2007), Chim et al (2008), Yuen (2007)and Papageorgiou et al (2009). The span of the loci included thepreviously reported genomic regions but in general spanned largerregions. “d” indicates that candidate DMRs that contained loci whichqualified as differentially methylated in the third trimester wereclassified as true-positive (TP) or false-negative (FN) based on therequirement of observing >0.20 difference between the methylationdensities of the corresponding genome coordinates of the DMRs inmaternal blood cells and term placental tissue. For loci that did notqualify as differentially methylated in the third trimester, theirabsence in the DMR list or the presence of a DMR containing the loci butshowing methylation difference of <0.20 was considered as true negative(TN) DMRs.

B. DMRs from the Maternal Plasma Sequencing Data

One should be able to identify placental tissue DMRs directly from thematernal plasma DNA bisulfate-sequencing data provided that thefractional fetal DNA concentration of the sample was also known. It ispossible because the placenta is the predominant source of fetal DNA inmaternal plasma (S S C Chim et al. 2005 Proc Natl Acad Sci USA 102,14753-14758) and we showed in this study that the methylation status offetal-specific DNA in maternal plasma correlated with the placentalmethylome.

Therefore, aspects of method 1400 may be implemented using a plasmamethylome to determine a deduced placental methylome instead of using aplacental sample. Thus, method 1000 and method 1400 can be combined todetermine DMRs. Method 1000 can be used to determine the predictedvalues for the placental methylation profile and use them in method1400. For this analysis, the example also focuses on loci that wereeither ≤20% or ≥80% methylated in the maternal blood cells.

In one implementation, to deduce loci that were hypermethylated in theplacental tissues with respect to maternal blood cells, we sorted forloci that showed ≤20% methylation in maternal blood cells, and ≥60%methylation according to the predicted value with a difference of atleast 50% between the blood cell methylation density and the predictedvalue. To deduce loci that were hypomethylated in the placental tissueswith respect to maternal blood cells, we sorted for loci that showed≥80% methylation in maternal blood cells, and ≤40% methylation accordingto the predicted value with a difference of at least 50% between theblood cell methylation density and the predicted value.

FIG. 16 is a table 1600 showing the numbers of loci predicted to behypermethylated or hypomethylated based on direct analysis of thematernal plasma bisulfite-sequencing data. “N/A” means not applicable.“a” indicates that the search for hypermethylated loci started from thelist of loci showing methylation densities <20% in the maternal bloodcells. “b” indicates that the search for hypomethylated loci startedfrom the list of loci showing methylation densities >80% in the maternalblood cells. “c” indicates that bisulfate-sequencing data from thechorionic villus sample was used for verifying the first trimestermaternal plasma data, and the term placental tissue was used forverifying the third trimester maternal plasma data.

As shown in table 1600, a majority of the noninvasively deduced locishowed the expected methylation pattern in the tissues and overlappedwith the DMRs mined from the tissue data and presented in the earliersection. The appendix of U.S. application Ser. No. 13/842,209 lists DMRsidentified from the plasma. Table S3A lists the top 100 loci deduced tobe hypermethylated from the first trimester maternal plasmabisulfate-sequencing data. Table S3B lists the top 100 loci deduced tobe hypomethylated from the first trimester maternal plasmabisulfate-sequencing data. Table S3C lists the top 100 loci deduced tobe hypermethylated from the third trimester maternal plasmabisulfate-sequencing data. Table S3D lists the top 100 loci deduced tobe hypomethylated from the third trimester maternal plasmabisulfate-sequencing data.

C. Gestational Variation in Placental and Fetal Methylomes

The overall proportion of methylated CpGs in the CVS was 55% while itwas 59% for the term placenta (table 100 of FIG. 1). More hypomethylatedDMRs could be identified from CVS than the term placenta while thenumber of hypermethylated DMRs was similar for the two tissues. Thus, itwas evident that the CVS was more hypomethylated than the term placenta.This gestational trend was also apparent in the maternal plasma data.The proportion of methylated CpGs among the fetal-specific reads was47.0% in the first trimester maternal plasma but was 53.3% in the thirdtrimester maternal plasma. The numbers of validated hypermethylated lociwere similar in the first (1,457 loci) and third trimester (1,279 loci)maternal plasma samples but there were substantially more hypomethylatedloci in the first (21,812 loci) than the third trimester (12,677 loci)samples (table 1600 of FIG. 16).

D. Use of Markers

The differentially methylated markers, or DMRs, are useful in severalaspects. The presence of such markers in maternal plasma indicates andconfirms the presence or fetal or placental DNA. This confirmation canbe used as a quality control for noninvasive prenatal testing. DMRs canserve as generic fetal DNA markers in maternal plasma and haveadvantages over markers that rely on genotypic differences between themother and fetus, such as polymorphism based markers or those based onchromosome Y. DMRs are generic fetal markers that are useful for allpregnancies. The polymorphism based markers are only applicable to thesubset of pregnancies where the fetus has inherited the marker from itsfather and where the mother does not possess this marker in her genome.In addition, one could measure the fetal DNA concentration in a maternalplasma sample by quantifying the DNA molecules originating from thoseDMRs. By knowing the profile of DMRs expected for normal pregnancies,pregnancy-associated complications, particularly those involvingplacental tissue changes, could be detected by observing a deviation inthe maternal plasma DMR profile or methylation profile from thatexpected for normal pregnancies. Pregnancy-associated complications thatinvolve placental tissue changes include but are not limited to fetalchromosomal aneuploidies. Examples include trisomy 21, preeclampsia,intrauterine growth retardation and preterm labor.

E. Kits Using Markers

Embodiments can provide compositions and kits for practicing the methodsdescribed herein and other applicable methods. Kits can be used forcarrying out assays for analyzing fetal DNA, e.g., cell-free fetal DNAin maternal plasma. In one embodiment, a kit can include at least oneoligonucleotide useful for specific hybridization with one or more lociidentified herein. A kit can also include at least one oligonucleotideuseful for specific hybridization with one or more reference loci. Inone embodiment, placental hypermethylated markers are measured. The testlocus may be the methylated DNA in maternal plasma and the referencelocus may be the methylated DNA in maternal plasma. A similar kit couldbe composed for analyzing tumor DNA in plasma.

In some cases, the kits may include at least two oligonucleotide primersthat can be used in the amplification of at least a section of a targetlocus (e.g., a locus in the appendix of U.S. application Ser. No.13/842,209) and a reference locus. Instead of or in addition to primers,a kit can include labeled probes for detecting a DNA fragmentcorresponding to a target locus and a reference locus. In variousembodiments, one or more oligonucleotides of the kit correspond to alocus in the tables of the appendix of U.S. application Ser. No.13/842,209. Typically, the kits also provide instruction manuals toguide users in analyzing test samples and assessing the state ofphysiology or pathology in a test subject.

In various embodiments, a kit for analyzing fetal DNA in a biologicalsample containing a mixture of fetal DNA and DNA from a female subjectpregnant with a fetus is provided. The kit may comprise one or moreoligonucleotides for specifically hybridizing to at least a section of agenomic region listed in tables S2A, S2B, S2C, S2D, S3A, S3B, S3C, andS3D. Thus, any number of oligonucleotides from across the tables arejust from one table may be used. The oligonucleotides may act asprimers, and may be organized as pairs of primers, where a paircorresponds to a particular region from the tables.

VI. Relationship of Size and Methylation Density

Plasma DNA molecules are known to exist in circulation in the form ofshort molecules, with the majority of molecules about 160 bp in length(Y M D Lo et al. 2010 Sci Transl Med; 2: 61ra91, Y W Zheng at al. 2012Clin Chem; 58: 549-558). Interestingly, our data revealed a relationshipbetween the methylation status and the size of plasma DNA molecules.Thus, plasma DNA fragment length is linked to DNA methylation level. Thecharacteristic size profiles of plasma DNA molecules suggest that themajority are associated with mononucleosomes, possibly derived fromenzymatic degradation during apoptosis.

Circulating DNA is fragmented in nature. In particular, circulatingfetal DNA is shorter than maternally-derived DNA in maternal plasmasamples (K C A Chan et al. 2004 Clin Chem; 50: 88-92). As paired-endalignment enables the size analysis of bisulfite-treated DNA, one couldassess directly if any correlation exists between the size of plasma DNAmolecules and their respective methylation levels. We explored this inthe maternal plasma as well as a non-pregnant adult female controlplasma sample.

Paired-end sequencing (which includes sequencing an entire molecule) forboth ends of each DNA molecule was used to analyze each sample in thisstudy. By aligning the pair of end sequences of each DNA molecule to thereference human genome and noting the genome coordinates of the extremeends of the sequenced reads, one can determine the lengths of thesequenced DNA molecules. Plasma DNA molecules are naturally fragmentedinto small molecules and the sequencing libraries for plasma DNA aretypically prepared without any fragmentation steps. Hence, the lengthsdeduced by the sequencing represented the sizes of the original plasmaDNA molecules.

In a previous study, we determined the size profiles of the fetal andmaternal DNA molecules in maternal plasma (Y M D Lo et al. 2010 SciTransl Med; 2: 61ra91). We showed that the plasma DNA molecules hadsizes that resembled mononucleosomes and fetal DNA molecules wereshorter than the maternal ones. In this study, we have determined therelationship of the methylation status of plasma DNA molecules to theirsizes.

A. Results

FIG. 17A is a plot 1700 showing size distribution of maternal plasma,non-pregnant female control plasma, placental and peripheral blood DNA.For the maternal sample and the non-pregnant female control plasma, thetwo bisulfite-treated plasma samples displayed the same characteristicsize distribution as previously reported (Y M D Lo et al. 2010 SciTransl Med; 2: 61ra91) with the most abundant total sequences of 166-167bp in length and a 10-bp periodicity of DNA molecules shorter than 143bp.

FIG. 17B is a plot 1750 of size distribution and methylation profile ofmaternal plasma, adult female control plasma, placental tissue and adultfemale control blood. For DNA molecules of the same size and containingat least one CpG site, their mean methylation density was calculated. Wethen plotted the relationship between the sizes of the DNA molecules andtheir methylation densities. Specifically, the mean methylation densitywas determined for each fragment length ranging from 50 bp up to 180 bpfor sequenced reads covering at least 1 CpG site. Interestingly, themethylation density increased with the plasma DNA size and peaked ataround 166-167 bp. This pattern, however, was not observed in theplacenta and control blood DNA samples which were fragmented using anultrasonicator system.

FIG. 18 shows plots of methylation densities and size of plasma DNAmolecules. FIG. 18A is a plot 1800 for the first trimester maternalplasma. FIG. 18B is a plot 1850 for the third trimester maternal plasma.Data for all the sequenced reads that covered at least one CpG site arerepresented by the blue curve 1805. Data for reads that also contained afetal-specific SNP allele are represented by the red curve 1810. Datafor reads that also contained a maternal-specific SNP allele arerepresented by the green curve 1815.

Reads that contained a fetal-specific SNP allele were considered to havederived from fetal DNA molecules. Reads that contained amaternal-specific SNP allele were considered to have derived frommaternal DNA molecules. In general, DNA molecules with high methylationdensities were longer in size. This trend was present in both the fetaland maternal DNA molecules in both the first and third trimesters. Theoverall sizes of the fetal DNA molecules were shorter than the maternalones as previously reported.

FIG. 19A shows a plot 1900 of methylation densities and the sizes ofsequenced reads for an adult non-pregnant female. The plasma DNA samplefrom the adult non-pregnant female also showed the same relationshipbetween the sizes and methylation state of the DNA molecules. On theother hand, the genomic DNA samples were fragmented by anultrasonication step before MPS analysis. As shown in plot 1900, thedata from the blood cell and placental tissue samples did not reveal thesame trend. Since the fragmentation of the cells is artificial, onewould expect to have no relationship of size and density. Since thenaturally fragmented DNA molecules in plasma do show a dependence onsize, it can be presumed that the lower methylation densities make itmore likely for molecules to break into smaller fragments.

FIG. 19B is a plot 1950 showing size distribution and methylationprofile of fetal-specific and maternal-specific DNA molecules inmaternal plasma. Fetal-specific and maternal-specific plasma DNAmolecules also exhibited the same correlation between fragment size andmethylation level. Both the fragment length of placenta-derived andmaternal circulating cell-free DNA increased with the methylation level.Moreover, the distribution of their methylation status did not overlapwith each other, suggesting that the phenomenon exists irrespective ofthe original fragment length of the sources of circulating DNAmolecules.

B. Method

Accordingly, a size distribution can be used to estimate a totalmethylation percentage of a plasma sample. This methylation measurementcan then be tracked during pregnancy, during cancer monitoring, orduring treatment by serial measurement of the size distributions of theplasma DNA according to the relationship shown in FIGS. 18A and 18B. Themethylation measurement can also be used to look for increased ordecreased release of DNA from an organ or a tissue of interest. Forexample, one can specifically look for DNA methylation signaturesspecific to a specific organ (e.g. the liver) and to measure theconcentrations of these signatures in plasma. As DNA is released intoplasma when cells die, an increase in levels could mean an increase incell death or damage in that particular organ or tissue. A decrease inlevel from a particular organ can mean that treatment to counter damageor pathological processes in that organ is under control.

FIG. 20 is a flowchart of a method 2000 for estimating a methylationlevel of DNA in a biological sample of an organism according toembodiments of the present invention. The methylation level can beestimated for a particular region of a genome or the entire genome. If aspecific region is desired, then DNA fragments only from that specificregion may be used.

At block 2010, amounts of DNA fragments corresponding to various sizesare measured. For each size of a plurality of sizes, an amount of aplurality of DNA fragments from the biological sample corresponding tothe size can be measured. For instance, the number of DNA fragmentshaving a length of 140 bases may be measured. The amounts may be savedas a histogram. In one embodiment, a size of each of the plurality ofnucleic acids from the biological sample is measured, which may be doneon an individual basis (e.g., by single molecule sequencing of a wholemolecule or just ends of the molecule) or on a group basis (e.g., viaelectrophoresis). The sizes may correspond to a range. Thus, an amountcan be for DNA fragments that have a size within a particular range.When paired-end sequencing is performed, the DNA fragments (asdetermined by the paired sequence reads) mapping (aligning) to aparticular region may be used to determine the methylation level of theregion.

At block 2020, a first value of a first parameter is calculated based onthe amounts of DNA fragments at multiple sizes. In one aspect, the firstparameter provides a statistical measure of a size profile (e.g., ahistogram) of DNA fragments in the biological sample. The parameter maybe referred to as a size parameter since it is determined from the sizesof the plurality of DNA fragments.

The first parameter can be of various forms. One parameter is thepercentage of DNA fragment of a particular size or range of sizesrelative to all DNA fragments or relative to DNA fragments of anothersize or range. Such a parameter is a number of DNA fragments at aparticular size divided by the total number of fragments, which may beobtained from a histogram (any data structure providing absolute orrelative counts of fragments at particular sizes). As another example, aparameter could be a number of fragments at a particular size or withina particular range divided by a number of fragments of another size orrange. The division can act as a normalization to account for adifferent number of DNA fragments being analyzed for different samples.A normalization can be accomplished by analyzing a same number of DNAfragments for each sample, which effectively provides a same result asdividing by a total number fragments analyzed. Additional examples ofparameters and about size analysis can be found in U.S. patentapplication Ser. No. 13/789,553, which is incorporated by reference forall purposes.

At block 2030, the first size value is compared to a reference sizevalue. The reference size value can be calculated from DNA fragments ofa reference sample. To determine the reference size values, themethylation profile can be calculated and quantified for a referencesample, as well as a value of the first size parameter. Thus, when thefirst size value is compared to the reference size value, a methylationlevel can be determined.

At block 2040, the methylation level is estimated based on thecomparison. In one embodiment, one can determine if the first value ofthe first parameter is above or below the reference size value, andthereby determine if the methylation level of the instant sample isabove or below the methylation level to the reference size value. Inanother embodiment, the comparison is accomplished by inputting thefirst value into a calibration function. The calibration function caneffectively compare the first value to calibration values (a set ofreference size values) by identifying the point on a curve correspondingto the first value. The estimated methylation level is then provided asthe output value of the calibration function.

Accordingly, one can calibrate a size parameter to a methylation level.For example, a methylation level can be measured and associated with aparticular size parameter for that sample. Then data points from varioussamples can be fit a calibration function. In one implementation,different calibration functions can be used for different subsets ofDNA. Thus, there may be some form of calibration based on priorknowledge about the relationship between methylation and size for aparticular subset of DNA. For example, the calibration for fetal andmaternal DNA could be different.

As shown above, the placenta is more hypomethylated when compared withmaternal blood, and thus the fetal DNA is smaller due to the lowermethylation. Accordingly, an average size of the fragments of a sample(or other statistical value) can be used to estimate the methylationdensity. As the fragment sizes can be measured using paired-endsequencing, rather than the potentially technically more complexmethylation-aware sequencing, this approach would potentially becost-effective if used clinically. This approach can be used formonitoring the methylation changes associated with the progress ofpregnancy, or with pregnancy-associated disorders such as preeclampsia,preterm labor and fetal disorders (such as those caused by chromosomalor genetic abnormalities or intrauterine growth retardation).

In another embodiment, this approach can be used for detecting andmonitoring cancer. For example, with the successful treatment of cancer,the methylation profile in plasma or another bodily fluid as measuredusing this size-based approach would change towards that of healthyindividuals without cancer. Conversely, in the event that the cancer isprogressing, then the methylation profile in plasma or another bodilyfluid would diverge from that of healthy individuals without cancer.

In summary, the hypomethylated molecules were shorter than thehypermethylated ones in plasma. The same trend was observed in both thefetal and maternal DNA molecules. Since DNA methylation is known toinfluence nucleosome packing, our data suggest that perhaps thehypomethylated DNA molecules were less densely packed with histones andwere therefore more susceptible to enzymatic degradation. On the otherhand, the data presented in FIGS. 18A and 18B also showed that despitethe fetal DNA being much more hypomethylated than the maternal reads,the size distribution of the fetal and maternal DNA does not separatefrom one another completely. In FIG. 19B, one can see that even for thesame size category, the methylation level of fetal- andmaternal-specific reads differ from one another. This observationsuggests that the hypomethylated state of fetal DNA is not the onlyfactor that accounted for its relative shortness with reference to thematernal DNA.

VII. Imprinting Status of Gene Loci

Fetal-derived DNA molecules can be detected which share the samegenotype but with different epigenetic signatures as the mother inmaternal plasma (L L M Poon et al. 2002 Clin Chem; 48: 35-41). Todemonstrate that the sequencing approach is sensitive in picking upfetal-derived DNA molecules in maternal plasma, we applied the samestrategy to detect the imprinted fetal alleles in maternal plasmasample. Two genomic imprinted regions were identified: H19(chr11:1,977,419-1,977,821, NCBI Build36/hg18) and MEST(chr7:129,917,976-129,920,347, NCBI Build36/hg18). Both of them containinformative SNPs for differentiation between the maternal and fetalsequences. For H19, a maternally expressed gene, the mother washomozygous (A/A) and the fetus was heterozygous (A/C) for the SNPrs2071094 (chr11:1,977,740) in the region. One of the maternal A alleleswas fully methylated and the other is unmethylated. In the placenta,however, the A allele was unmethylated while the paternal-inherited Callele was fully methylated. We detected two methylated reads with the Cgenotype, corresponding to the imprinted paternal alleles derived fromthe placenta, in maternal plasma.

MEST, also known as PEG1, is a paternally expressed gene. Both themother and the fetus were heterozygous (A/G) for the SNP rs2301335(chr7:129,920,062) within the imprinted locus. The G allele wasmethylated while the A allele was unmethylated in maternal blood. Themethylation pattern was reversed in the placenta with the maternal Aallele being methylated and the paternal G allele unmethylated. Threeunmethylated G alleles, which were paternally derived, were detectablein maternal plasma. In contrast, VAV1, a non-imprinted gene locus onchromosome 19 (chr19:6,723,621-6,724,121), did not display any allelicmethylation pattern in the tissue as well as in the plasma DNA samples.

Thus, methylation status can be used to determine which DNA fragmentsare from the fetus. For example, just detecting the A allele in maternalplasma cannot be used as a fetal marker when the mother is GAheterozygous. But if one distinguishes the methylation status of the Amolecules in plasma, the methylated A molecules are fetal-specific whilethe unmethylated A-molecules are maternal-specific, or vice versa.

We next focused on loci that have been reported to demonstrate genomicimprinting in placental tissues. Based on the list of loci reported byWoodfine et al. (2011 Epigenetics Chromatin; 4: 1), we further sortedfor those that contained SNPs within the imprinting control region. Fourloci fulfilled the criteria and they were H19, KCNQ10T1, MEST and NESP.

Regarding the reads of the maternal blood cell sample for H19 andKCNQ10T1, the maternal reads were homozygous for the SNP and there wereapproximately equal proportions of methylated and unmethylated reads.The CVS and term placental tissue sample revealed that the fetus washeterozygous for both loci and each allele was either exclusivelymethylated or unmethylated, i.e. showing monoallelic methylation. In thematernal plasma samples, the paternally inherited fetal DNA moleculeswere detected for both loci. For H19, the paternally inherited moleculeswere represented by the sequenced reads that contained thefetal-specific allele and were methylated. For KCNQ10T1, the paternallyinherited molecules were represented by the sequenced reads thatcontained the fetal-specific allele and were unmethylated.

On the other hand, the mother was heterozygous for both MEST and NESP.For MEST, both the mother and fetus were GA heterozygotes for the SNP.However, as evident from the data for the Watson strand for the maternalblood cells and placental tissue, the methylation status for the CpGsadjacent to the SNP was opposite in the mother and fetus. The A-allelewas unmethylated in the mother's DNA but methylated in the fetus's DNA.For MEST, the maternal allele was methylated. Hence, one could pinpointthat the fetus had inherited the A-allele from its mother (methylated inthe CVS) and the mother had inherited the A-allele from her father(unmethylated in the maternal blood cells). Interestingly, in thematernal plasma samples, all four groups of molecules could be readilydistinguished, including each of the two alleles of the mother and eachof the two alleles of the fetus. Thus, by combining the genotypeinformation with the methylation status at the imprinted loci, we couldreadily distinguish the maternally inherited fetal DNA molecules fromthe background maternal DNA molecules (L L M Poon et al. 2002 Clin Chem;48: 35-41).

This approach could be used to detect uniparental disomy. For example,if the father of this fetus is known to be homozygous for the G-allele,the failure to detect the unmethylated G-allele in maternal plasmasignifies the lack of contribution of the paternal allele. In addition,under such a circumstance, when both methylated G-allele and methylatedA-allele were detected in the plasma of this pregnancy, it would suggestthat the fetus has heterodisomy from the mother, i.e. inheriting twodifferent alleles from the mother with no inheritance from the father.Alternatively, if both methylated A-allele (fetal allele inherited fromthe mother) and unmethylated A-allele (maternal allele inherited fromthe maternal grandfather) were detected in maternal plasma without theunmethylated G-allele (paternal allele that should have been inheritedby the fetus), it would suggest that the fetus has isodisomy from themother, i.e. inheriting two identical alleles from the mother with noinheritance from the father.

For NESP, the mother was a GA heterozygote at the SNP while the fetuswas homozygous for the G-allele. The paternal allele was methylated forNESP. In the maternal plasma samples, the paternally-inherited fetalG-alleles that were methylated could be readily distinguished from thebackground maternal G-alleles which were unmethylated.

VIII. Cancer/Donors

Some embodiments can be used for the detection, screening, monitoring(e.g. for relapse, remission, or response (e.g. presence or absence) totreatment), staging, classification (e.g. for aid in choosing the mostappropriate treatment modality) and prognostication of cancer usingmethylation analysis of circulating plasma/serum DNA.

Cancer DNA is known to demonstrate aberrant DNA methylation (JG Hermanet al. 2003 N Engl J Med; 349: 2042-2054). For example, the CpG islandpromoters of genes, e.g. tumor suppressor genes, are hypermethylatedwhile the CpG sites in the gene body are hypomethylated when comparedwith non-cancer cells. Provided that the methylation profile of thecancer cells could be reflected by the methylation profile of thetumor-derived plasma DNA molecules using methods herein described, weexpect that the overall methylation profile in plasma would be differentbetween individuals with cancer when compared with those healthyindividuals without cancer or when compared with those whose cancer hadbeen cured. The types of differences in the methylation profile could bein terms of quantitative differences in the methylation densities of thegenome and/or methylation densities of segments of the genomes. Forexample, due to the general hypomethylated nature of DNA from cancertissues (Gama-Sosa M A et al. 1983 Nucleic Acids Res; 11: 6883-6894),reduction in methylation densities in the plasma methylome or segmentsof the genome would be observed in plasma of cancer patients.

Qualitative changes in the methylation profile should also be reflectedamong the plasma methylome data. For example, plasma DNA moleculesoriginating from genes that are hypermethylated only in cancer cellswould show hypermethylation in plasma of a cancer patient when comparedwith plasma DNA molecules originating from the same genes but in asample of a healthy control. Because aberrant methylation occurs in mostcancers, the methods herein described could be applied to the detectionof all forms of malignancies with aberrant methylation, for example,malignancies in, but not limited to, the lung, breast, colorectum,prostate, nasopharynx, stomach, testes, skin, nervous system, bone,ovary, liver, hematologic tissues, pancreas, uterus, kidney, bladder,lymphoid tissues, etc. The malignancies may be of a variety ofhistological subtypes, for example, carcinomas, adenocarcinomas,sarcomas, fibroadenocarcinoma, neuroendocrine, and undifferentiated,etc.

On the other hand, we expect that tumor-derived DNA molecules can bedistinguished from the background non-tumor-derived DNA moleculesbecause the overall short size profile of tumor-derived DNA isaccentuated for DNA molecules originating from loci withtumor-associated aberrant hypomethylation which would have an additionaleffect on the size of the DNA molecule. Also, tumor-derived plasma DNAmolecules can be distinguished from the background non-tumor-derivedplasma DNA molecules using multiple characteristic features that areassociated with tumor DNA, including but not limited to singlenucleotide variants, copy number gains and losses, translocations,inversions, aberrant hyper- or hypo-methylation and size profiling. Asall of these changes could occur independently, the combined use ofthese features may provide additive advantage for the sensitive andspecific detection of cancer DNA in plasma.

A. Size and Cancer

The size of tumor-derived DNA molecules in plasma also resemble thesizes of mononucleosomal units and are shorter than the backgroundnon-tumor-derived DNA molecules, which co-exists in plasma of cancerpatients. Size parameters have been shown to be correlated with cancer,as described in U.S. patent application Ser. No. 13/789,553, which isincorporated by reference for all purposes.

Since both fetal-derived and maternal-derived DNA in plasma showed arelationship between the size and methylation status of the molecule,tumor-derived DNA molecules are expected to exhibit the same trend. Forexample, the hypomethylated molecules would be shorter than thehypermethylated molecules in the plasma of cancer patients or insubjects screened for cancer.

B. Methylation Densities of Different Tissues in a Cancer Patient

In this example, we analyzed the plasma and tissue samples of ahepatocellular carcinoma (HCC) patient. Blood samples were collectedfrom the HCC patient before and at 1 week after surgical resection ofthe tumor. Plasma and buffy coat were harvested after centrifugation ofthe blood samples. The resected tumor and the adjacent non-tumor livertissue were collected. The DNA samples extracted from the plasma andtissue samples were analyzed using massively parallel sequencing withand without prior bisulfite treatment. The plasma DNA from four healthyindividuals without cancer was also analyzed as controls. The bisulfitetreatment of a DNA sample would convert the unmethylated cytosineresidues to uracil. In the downstream polymerase chain reaction andsequencing, these uracil residues would behave as thymidine. On theother hand, the bisulfite treatment would not convert the methylatedcytosine residues to uracil. After massively parallel sequencing, thesequencing reads were analyzed by the Methy-Pipe (P Jiang, et al.Methy-Pipe: An integrated bioinformatics data analysis pipeline forwhole genome methylome analysis, paper presented at the IEEEInternational Conference on Bioinformatics and Biomedicine Workshops,Hong Kong, 18 to 21 Dec. 2010), to determine the methylation status ofthe cytosine residues at all CG dinucleotide positions, i.e CpG sites.

FIG. 21A is a table 2100 showing the methylation densities of thepre-operative plasma and the tissue samples of an HCC patient. The CpGmethylation density for the regions of interest (e.g. CpG sites,promoter, or repeat regions etc.) refers to the proportion of readsshowing CpG methylation over the total number of reads covering genomicCpG dinucleotides. The methylation densities of the buffy coat and thenon-tumoral liver tissue are similar. The overall methylation density ofthe tumor tissue, based on data from all autosomes, was 25% lower thanthose of the buffy coat and the non-tumoral liver tissue. Thehypomethylation was consistent across each individual chromosome. Themethylation density of the plasma was between the values of thenon-malignant tissues and the cancer tissues. This observation isconsistent with the fact that both cancer and non-cancer tissues wouldcontribute to the circulating DNA of a cancer patient. It has been shownthat the hematopoietic system is the main source of the circulating DNAin individuals without an active malignant condition (Y Y N Lui, et al.2002 Clin Chem; 48: 421-7). We therefore also analyzed plasma samplesobtained from four healthy controls. The number of sequence reads andthe sequencing depth achieved per sample are shown in table 2150 of FIG.21B.

FIG. 22 is a table 2200 showing the methylation densities in theautosomes ranged from 71.2% to 72.5% in the plasma samples of thehealthy controls. These data showed the expected level of DNAmethylation in plasma samples obtained from individuals without a sourceof tumor DNA. In a cancer patient, the tumor-tissue would also releaseDNA into the circulation (K C A Chan et al. 2013 Clin Chem; 59:211-224); R J Leary et al. 2012 Sci Transl Med; 4: 162ra154). Due to thehypomethylated nature of the HCC tumor, the presence of both tumor- andnon-tumor-derived DNA in the pre-operative plasma of the patientresulted in a reduction in the methylation density when compared withplasma levels of healthy controls. In fact, the methylation density ofthe pre-operative plasma sample was between the methylation densities ofthe tumor tissue and the plasma of the healthy controls. The reason isbecause the methylation level of the plasma DNA of cancer patients wouldbe influenced by the degree of aberrant methylation, hypomethylation inthis case, of the tumor tissue and the fractional concentration of thetumor-derived DNA in the circulation. A lower methylation density of thetumor tissue and a higher fractional concentration of tumor-derived DNAin the circulation would lead to a lower methylation density of theplasma DNA in a cancer patient. Most tumors are reported to show globalhypomethylation (JG Herman et al. 2003 N Engl J Med; 349: 2042-2054; MAGama-Sosa et al. 1983 Nucleic Acids Res; 11: 6883-6894). Thus, thecurrent observations seen in the HCC samples should also be applicableto other types of tumors.

In one embodiment, the methylation density of the plasma DNA can be usedto determine the fractional concentration of tumor-derived DNA in aplasma/serum sample when the methylation level of the tumor tissue isknown. The methylation level, e.g. methylation density, of the tumortissue can be obtained if the tumor sample is available or a biopsy ofthe tumor is available. In another embodiment, the information regardingthe methylation level of the tumor tissue can be obtained from survey ofthe methylation level in a group of tumors of a similar type and thisinformation (e.g. a mean level or a median level) is applied to thepatient to be analyzed using the technology described in this invention.The methylation level of the tumor tissue can be determined by theanalysis of the tumor tissue of the patient or inferred from theanalysis of the tumor tissues of other patients with the same or asimilar cancer type. The methylation of tumor tissues can be determinedusing a range of methylation-aware platforms, including but not limitedto massively parallel sequencing, single molecular sequencing,microarray (e.g. oligonucleotide arrays), or mass spectrometry (such asthe Epityper, Sequenom, Inc., analysis). In some embodiments, suchanalyses may be preceded by procedures that are sensitive to themethylation status of DNA molecules, including, but not limited to,cytosine immunoprecipitation and methylation-aware restriction enzymedigestion. When the methylation level of a tumor is known, thefractional concentration of tumor DNA in the plasma of cancer patientscould be calculated after plasma methylome analysis.

The relationship between the plasma methylation level, P, with thefractional tumor DNA concentration, f, and the tumor tissue methylationlevel, TUM, can be described as: P=BKG×(1−f)+TUM×f, where BKG is thebackground DNA methylation level in plasma derived from blood cells andother internal organs. For example, the overall methylation density ofall autosomes was shown to be 42.9% in the tumor biopsy tissue obtainedfrom this HCC patient, i.e. the TUM value for this case. The meanmethylation density of the plasma samples from the four healthy controlswas 71.6%, i.e. the BKG value of this case. The plasma methylationdensity for the pre-operative plasma was 59.7%. Using these values, f isestimated to be 41.5%.

In another embodiment, the methylation level of the tumor tissue can beestimated noninvasively based on the plasma methylome data when thefractional concentration of the tumor-derived DNA in the plasma sampleis known. The fractional concentration of the tumor-derived DNA in theplasma sample can be determined by other genetic analysis, for examplethe genomewide analysis of allelic loss (GAAL) and the analysis ofsingle nucleotide mutations as previously described (U.S. patentapplication Ser. No. 13/308,473; K C A Chan et al. 2013 Clin Chem; 59:211-24). The calculation is based on the same relationship describedabove except that in this embodiment, the value off is known and thevalue of TUM becomes the unknown. The deduction can be performed for thewhole genome or for parts of the genome, similar to the data observedfor the context of determining the placental tissue methylation levelfrom maternal plasma data.

In another embodiment, one can use the inter-bin variation or profile inthe methylation densities to differentiate subjects with cancer andthose without cancer. The resolution of the methylation analysis can befurther increased by dividing the genome into bins of a particular size,e.g., 1 Mb. In such an embodiment, the methylation density of each 1 Mbbin was calculated for the collected samples, e.g., buffy coat, theresected HCC tissue, the non-tumoral liver tissue adjacent to the tumorand the plasma collected before and after tumor resection. In anotherembodiment, the bin sizes do not need to be kept constant. In oneimplementation, the number of CpG sites is kept constant within each binwhile the bin itself can vary in size.

FIGS. 23A and 23B show methylation density of buffy coat, tumor tissue,non-tumoral liver tissue, the pre-operative plasma and post-operativeplasma of the HCC patient. FIG. 23A is a plot 2300 of results forchromosome 1. FIG. 23B is a plot 2350 of results for chromosome 2.

For most of the 1 Mb windows, the methylation densities for the buffycoat and the non-tumoral liver tissue adjacent to the tumor were similarwhereas those of the tumor tissues were lower. The methylation densitiesof the pre-operative plasma lie between those of the tumor and thenon-malignant tissues. The methylation densities of the interrogatedgenomic regions in the tumor tissues could be deduced using themethylation data of the pre-operative plasma and the fractional tumorDNA concentration. The method is same as described above using themethylation density values of all the autosomes. The deduction of thetumor methylation described can also be performed using this higherresolution methylation data of the plasma DNA. Other bin sizes, such as300 kb, 500 kb, 2 Mb, 3 Mb, 5 Mb or more than 5 Mb can also be used. Inone embodiment, the bin sizes do not need to be kept constant. In oneimplementation, the number of CpG sites is kept constant within each binwhile the bin itself can vary in size.

C. Comparison of Plasma Methylation Density Between the Cancer Patientand Healthy Individuals

As shown in 2100, the methylation densities of the pre-operative plasmaDNA were lower than those of the non-malignant tissues in the cancerpatient. This is likely to result from the presence of DNA from thetumor tissue which was hypomethylated. This lower plasma DNA methylationdensity can potentially be used as a biomarker for the detection andmonitoring of cancer. For cancer monitoring, if a cancer is progressing,then there will be an increased amount of cancer-derived DNA in plasmawith time. In this example, an increased amount of circulatingcancer-derived DNA in plasma will lead to a further reduction in theplasma DNA methylation density on a genomewide level.

Conversely, if a cancer responds to treatment, then the amount ofcancer-derived DNA in plasma will decrease with time. In this example, adecrease in the amount of cancer-derived DNA in plasma will lead to anincrease in the plasma DNA methylation density. For example, if a lungcancer patient with epidermal growth factor receptor mutation has beentreated with a targeted therapy, e.g. tyrosine kinase inhibition, thenan increase in plasma DNA methylation density would signify a response.Subsequently, the emergence of a tumor clone resistant to tyrosinekinase inhibition would be associated with a decrease in plasma DNAmethylation density which would indicate a relapse.

Plasma methylation density measurements can be performed serially andthe rate of change of such measurements can be calculated and used topredict or correlate with clinical progression or remission orprognosis. For selected genomic loci which are hypermethylated in cancertissues but hypomethylated in normal tissues, e.g. the promoter regionsof a number of tumor suppressor genes, the relationship between cancerprogression and favorable response to treatment will be opposite to thepatterns described above.

To demonstrate the feasibility of this approach, we compared the DNAmethylation densities of plasma samples collected from the cancerpatient before and after surgical removal of the tumor with plasma DNAobtained from four healthy control subjects.

Table 2200 shows the DNA methylation densities of each autosome and thecombined values of all autosomes of the pre-operative and post-operativeplasma samples of the cancer patient and that of the four healthycontrol subjects. For all chromosomes, the methylation densities of thepre-operative plasma DNA sample were lower than those of thepost-operative sample and the plasma samples from the four healthysubjects. The difference in the plasma DNA methylation densities betweenthe pre-operative and post-operative samples provided supportiveevidence that the lower methylation densities in the pre-operativeplasma sample were due to the presence of DNA from the HCC tumor.

The reversal of the DNA methylation densities in the post-operativeplasma sample to levels similar to the plasma samples of the healthycontrols suggested that much of the tumor-derived DNA had disappeareddue to the surgical removal of the source, i.e. the tumor. These datasuggest that the methylation density of the pre-operative plasma asdetermined using data available from a large genomic regions, such asall autosomes or individual chromosomes, was of a lower methylationlevel than that of the healthy controls to allow the identification,i.e. diagnosis or screening, of the test case as having cancer.

The data of the pre-operative plasma also showed much lower methylationlevel than that of the post-operative plasma indicating that the plasmamethylation level could also be used to monitor the tumor load, hence toprognosticate and monitor the progress of cancer in the patient.Reference values can be determined from plasma of healthy controls orpersons at-risk for the cancer but currently without cancer. Persons atrisk for HCC include those with chronic hepatitis B or hepatitis Cinfection, those with hemochromatosis, and those with liver cirrhosis.

Plasma methylation density values beyond, for example lower than, adefined cutoff based on the reference values can be used to assess if anonpregnant person's plasma has tumor DNA or not. To detect the presenceof hypomethylated circulating tumor DNA, the cutoff can be defined aslower than the 5^(th) or 1^(st) percentiles of the values of the controlpopulation, or based on a number of standard deviations, for example, 2or 3 standard deviations (SDs), below the mean methylation densityvalues of the controls, or based on determining a multiple of the median(MoM). For hypermethylated tumor DNA, the cutoff can be defined ashigher than the 95^(th) or 99^(th) percentile of the values of thecontrol population, or based on a number of standard deviations, forexample, 2 or 3 SDs, above the mean methylation density values of thecontrols, or based on determining a multiple of the median (MoM). In oneembodiment, the control population is matched in age to the testsubject. The age matching does not need to be exact and can be performedin age bands (e.g. 30 to 40 years, for a test subject of 35 years).

We next compared the methylation densities of 1 Mb bins between theplasma samples of the cancer patient and the four control subjects. Forillustration purpose, the results of chromosome 1 are shown.

FIG. 24A is a plot 2400 showing the methylation densities of thepre-operative plasma from the HCC patient. FIG. 24B is a plot 2450showing the methylation densities of the post-operative plasma from theHCC patient. The blue dots represent the results of the controlsubjects, the red dots represent the results of the plasma sample of theHCC patient.

As shown in FIG. 24A, the methylation densities of the pre-operativeplasma from the HCC patient were lower than those of the controlsubjects for most of the bins. Similar patterns were observed for otherchromosomes. As shown in FIG. 24B, the methylation densities of thepost-operative plasma from the HCC patient were similar to those of thecontrol subjects for most of the bins. Similar patterns were observedfor other chromosomes.

To assess if a tested subject is having cancer, the result of the testedsubject would be compared to the values of a reference group. In oneembodiment, the reference group can comprise of a number of healthysubjects. In another embodiment, the reference group can comprise ofsubjects with non-malignant conditions, for example, chronic hepatitis Binfection or cirrhosis. The difference in the methylation densitiesbetween the tested subject and the reference group can then bequantified.

In one embodiment, a reference range can be derived from the values ofthe control group. Then deviations in the result of the tested subjectfrom the upper or lower limits of the reference group can be used todetermine if the subject has a tumor. This quantity would be affected bythe fractional concentration of tumor-derived DNA in the plasma and thedifference in the level of methylation between malignant andnon-malignant tissues. Higher fractional concentration of tumor-derivedDNA in plasma would lead to larger methylation density differencesbetween the test plasma sample and the controls. A larger degree ofdifference in the methylation level of the malignant and non-malignanttissues is also associated with larger methylation density differencesbetween the test plasma sample and the controls. In yet anotherembodiment, different reference groups are chosen for test subjects ofdifferent age ranges.

In another embodiment, the mean and SD of the methylation densities ofthe four control subjects were calculated for each 1 Mb bin. Then forcorresponding bins, the difference between the methylation densities ofthe HCC patient and the mean value of the control subjects wascalculated. In one embodiment, this difference was then divided by theSD of the corresponding bin to determine the z-score. In other words,the z-score represents the difference in methylation densities betweenthe test and control plasma samples expressed as a number of SDs fromthe mean of the control subjects. A z-score >3 of a bin indicates thatthe plasma DNA of the HCC patient is more hypermethylated than thecontrol subjects by more than 3 SDs in that bin whereas a z-score of <−3in a bin indicates that the plasma DNA of the HCC patient is morehypomethylated than the control subjects by more than 3 SDs in that bin.

FIGS. 25A and 25B show z-scores of the plasma DNA methylation densitiesfor the pre-operative (plot 2500) and post-operative (plot 2550) plasmasamples of the HCC patient using the plasma methylome data of the fourhealthy control subjects as reference for chromosome 1. Each dotrepresents the result of one 1 Mb bin. The black dots represent the binswith z-score between −3 and 3. Red dots represent bins with z-score <−3.

FIG. 26A is a table 2600 showing data for z-scores for pre-operative andpost-operative plasma. Most of the bins on chromosome 1 (80.9%) in thepre-operative plasma sample had a z-score of <−3 indicating that thepre-operative plasma DNA of the HCC patient was significantly morehypomethylated than that of the control subjects. On the contrary, thenumber of red dots decreased substantially in the post-operative plasmasample (8.3% of the bins on chromosome 1) suggesting that most of thetumor DNA had been removed from the circulation due to surgicalresection of the source of circulating tumor DNA.

FIG. 26B is a Circos plot 2620 showing the z-score of the plasma DNAmethylation densities for the pre-operative and post-operative plasmasamples of the HCC patient using the four healthy control subjects asreference for 1 Mb bins analyzed from all autosomes. The outermost ringshows the ideograms of the human autosomes. The middle ring shows thedata for the pre-operative plasma sample. The innermost ring shows thatdata for the post-operative plasma sample. Each dot represents theresult of one 1 Mb bin. The black dots represent the bins with z-scoresbetween −3 and 3. The red dots represent bins with z-scores <−3. Thegreen dots represent bins with z-scores >3.

FIG. 26C is a table 2640 showing a distribution of the z-scores of the 1Mb bins for the whole genome in both the pre-operative andpost-operative plasma samples of the HCC patient. The results indicatethat the pre-operative plasma DNA of the HCC patient was morehypomethylated than that of the controls for the majority of regions(85.2% of the 1 Mb bins) in the whole genome. On the contrary, majorityof the regions (93.5% of the 1 Mb bins) in the post-operative plasmasample showed no significant hypermethylation or hypomethylationcompared with controls. These data indicate that much of the tumor DNA,mainly hypomethylated in nature for this HCC, was no longer present inthe post-operative plasma sample.

In one embodiment, the number, percentage or proportion of bins withz-scores <−3 can be used to indicate if a cancer is present. Forexample, as shown in table 2640, 2330 of the 2734 bins analyzed (85.2%)showed z-scores <−3 in the pre-operative plasma while only 171 of the2734 analyzed bins (6.3%) showed z-scores <−3 in the post-operativeplasma. The data indicated that the tumor DNA load in the pre-operativeplasma was much higher than in the post-operative plasma.

The cutoff values of the number of bins may be determined usingstatistical methods. For example, approximately 0.15% of the bins wouldbe expected to have a z-score of <−3 based on a normal distribution.Therefore, the cutoff number of bins can be 0.15% of the total number ofbins being analyzed. In other words, if a plasma sample from anonpregnant individual shows more than 0.15% of bins with z-scores <−3,there is a source of hypomethylated DNA in plasma, namely cancer. Forexample, 0.15% of the 2734 1 Mb bins that we have analyzed in thisexample is about 4 bins. Using this value as a cutoff, both thepre-operative and post-operative plasma samples contained hypomethylatedtumor-derived DNA, though the amount is much more in the pre-operativeplasma sample than the post-operative plasma sample. For the fourhealthy control subjects, none of the bins showed significanthypermethylation or hypomethylation. Other cutoff values (e.g., 1.1%)can be used and can vary depending of the requirement of the assay beingused. As other examples, the cutoff percentage can vary based on thestatistical distribution, as well as the sensitivity desired and anacceptable specificity.

In another embodiment, the cutoff number can be determined by receiveroperator characteristic (ROC) curve analysis by analyzing a number ofcancer patients and individuals without cancer. To further validate thespecificity of this approach, a plasma sample from a patient seekingmedical consultation for a non-malignant condition (C06) was analyzed.1.1% of the bins had a z-score of <−3. In one embodiment, differentthresholds can be used to classify different levels of disease status. Alower percentage threshold can be used to differentiate healthy statusfrom benign conditions and a higher percentage threshold todifferentiate benign conditions from malignancies.

The diagnostic performance for plasma hypomethylation analysis usingmassively parallel sequencing appears to be superior than that obtainedusing polymerase chain reaction (PCR)-based amplification of specificclasses of repetitive elements, e.g. long interspersed nuclear element-1(LINE-1) (P Tangkijvanich et al. 2007 Clin Chim Acta; 379:127-133). Onepossible explanation for this observation is that while hypomethylationis pervasive in the tumor genome, it does have some degree ofheterogeneity from one genomic region to the next.

In fact, we observed that the mean plasma methylation densities of thereference subjects varied across the genome (FIG. 56). Each red dot inFIG. 56 shows the mean methylation density of one 1 Mb bin among 32healthy subjects. The plot shows all 1 Mb bins analyzed across thegenome. The number within each box represents the chromosome number. Weobserved that the mean methylation densities varied from bin to bin.

A simple PCR-based assay would not be able to take account of suchregion-to-region heterogeneity into its diagnostic algorithm. Suchheterogeneity would broaden the range of methylation densities observedamong the healthy individuals. A greater magnitude of reduction in themethylation density would then be needed for a sample to be consideredas showing hypomethylation. This would result in a reduction of the testsensitivity.

In contrast, a massively parallel sequencing-based approach divides thegenome into 1 Mb bins (or other sized bin) and measures the methylationdensities for such bins individually. This approach reduces the impactof the variations in the baseline methylation densities across differentgenomic regions as each region is compared between a test sample and thecontrols. Indeed, within the same bin, the inter-individual variationacross the 32 healthy controls was relatively small. 95% of the bins hada coefficient of variation (CV) across the 32 healthy controls of ≤1.8%.Yet, to further enhance the sensitivity for the detection ofcancer-associated hypomethylation, the comparison can be performedacross multiple genomic regions. The sensitivity would be enhanced bytesting multiple genomic regions because it would safeguard against theeffect of biological variation when the cancer sample happens to notdemonstrate hypomethylation for a particular region when just one regionis tested.

The approach of comparing the methylation densities of equivalentgenomic regions between controls and test samples (e.g., testing eachgenomic region separately, and then possibly combing such results) andperform this comparison for multiple genomic regions has a highersignal-to-noise ratio for the detection of hypomethylation associatedwith cancer. This massively parallel sequencing approach is shown by wayof illustration. Other methodologies that could determine themethylation densities of multiple genomic regions and allow thecomparison of methylation densities of corresponding regions betweencontrols and test samples would be predicted to achieve similar effect.For example, hybridization probes or molecular inversion probes thatcould target plasma DNA molecules originating from specific genomicregions as well as determine a methylation level of the region could bedesigned to achieve the desired effect.

In yet another embodiment, the sum of the z-scores for all the bins canbe used to determine if cancer is present or used for the monitoring ofthe serial changes of the level of plasma DNA methylation. Due to theoverall hypomethylated nature of tumor DNA, the sum of z-scores would belower in plasma collected from an individual with cancer than healthycontrols. The sum of z-scores for the pre- and post-operative plasmasample of the HCC patient were −49843.8 and −3132.13, respectively.

In other embodiments, other methods can be used to survey themethylation level of plasma DNA. For example, the proportion ofmethylated cytosine residues over the total content of cytosine residuescan be determined using mass spectrometry (ML Chen et al. 2013 ClinChem; 59: 824-832) or massively parallel sequencing. However, as most ofthe cytosine residues are not in the CpG dinucleotide context, theproportion of methylated cytosine among total cytosine residues would berelatively small when compared to methylation levels estimated in thecontext of CpG dinucleotides. We determined the methylation level of thetissue and plasma samples obtained from the HCC patient as well as thefour plasma samples obtained from the healthy controls. The methylationlevels were measured in the context of CpGs, any cytosines, in CHG andCHH contexts using the genome-wide massively parallel sequencing data. Hrefers to adenine, thymine or cytosine residues.

FIG. 26D is a table 2660 showing the methylation levels of the tumortissue and pre-operative plasma sample overlapping with some of thecontrol plasma samples when using the CHH and CHG contexts. Themethylation levels of the tumor tissue and pre-operative plasma samplewere consistently lower when compared with the buffy coat, non-tumorliver tissue, post-operative plasma sample and healthy control plasmasamples in both among the CpGs and unspecified cytosines. However, thedata based on the methylated CpGs, i.e. methylation densities, showed awider dynamic range than the data based on the methylated cytosines.

In other embodiments, the methylation status of the plasma DNA can bedetermined by methods using antibodies against methylated cytosine, forexample, methylated DNA immunoprecipitation (MeDIP). However, theprecision of these methods are expected to be inferior tosequencing-based methods because of the variability in antibody binding.In yet another embodiment, the level of 5-hydroxymethylcytosine inplasma DNA can be determined. In this regard, a reduction in the levelof 5-hydroxymethylcytosine has been found to be an epigenetic feature ofcertain cancer, e.g. melanoma (C G Lian, et al. 2012 Cell; 150:1135-1146).

In addition to HCC, we also investigated if this approach could beapplied to other types of cancers. We analyzed the plasma samples from 2patients with adenocarcinoma of the lung (CL1 and CL2), 2 patients withnasopharyngeal carcinoma (NPC1 and NPC2), 2 patients with colorectalcancer (CRC1 and CRC2), 1 patient with metastatic neuroendocrine tumor(NE1) and 1 patient with metastatic smooth muscle sarcoma (SMS1). Theplasma DNA of these subjects was bisulfite-converted and sequenced usingthe Illumina HiSeq2000 platform for 50 bp at one end. The four healthycontrol subjects mentioned above were used as a reference group for theanalysis of these 8 patients. 50 bp of the sequence reads at one endwere used. The whole genome was divided into 1 Mb bins. The mean and SDof methylation density were calculated for each bin using the data fromthe reference group. Then the results of the 8 cancer patients wereexpressed as z-scores which represent the number of SDs from the mean ofthe reference group. A positive value indicates that the methylationdensity of the test case is lower than the mean of the reference group,and vice versa. The number of sequence reads and the sequencing depthachieved per sample are shown in table 2780 of FIG. 27I.

FIG. 27A-H show Circos plots of methylation density of 8 cancer patientsaccording to embodiments of the present invention. Each dot representsthe result of a 1 Mb bin. The black dots represent the bins withz-scores between −3 and 3. The red dots represent bins with z-scores<−3. The green dots represent bins with z-scores >3. The intervalbetween two consecutive lines represents a z-score difference of 20.

Significant hypomethylation was observed in multiple regions across thegenomes for patients with most types of cancers, including lung cancer,nasopharyngeal carcinoma, colorectal cancer and metastaticneuroendocrine tumor. Interestingly, in addition to hypomethylation,significant hypermethylation was observed in multiple regions across thegenome in the case with metastatic smooth muscle sarcoma. The embryonicorigin of the smooth muscle sarcoma is the mesoderm whereas theembryonic origin of the other types of cancers in the remaining 7patients is the ectoderm. Therefore, it is possible that the DNAmethylation pattern of sarcoma may be different from that of carcinoma.

As can be seen from this case, the methylation pattern of plasma DNA canalso be useful for differentiating different types of cancer, which inthis example is a differentiation of carcinoma and sarcoma. These dataalso suggest that the approach could be used to detect aberranthypermethylation associated with the malignancy. For all these 8 cases,only plasma samples were available and no tumor tissue had beenanalyzed. This showed that even without the prior methylation profile ormethylation levels of the tumor tissue, tumor-derived DNA can be readilydetected in plasma using the methods described.

FIG. 27J is a table 2790 is a table showing a distribution of thez-scores of the 1 Mb bins for the whole genome in plasma of patientswith different malignancies. The percentages of bins with z-score <−3,−3 to 3 and >3 are shown for each case. More than 5% of the bins had az-score of <−3 for all the cases. Therefore, if we use a cutoff of 5% ofthe bins being significantly hypomethylated for classifying a samplebeing positive for cancer, then all of these cases would be classifiedas positive for cancer. Our results show that hypomethylation is likelyto be a general phenomenon for different types of cancers and the plasmamethylome analysis would be useful for detecting different types ofcancers.

D. Method

FIG. 28 is a flowchart of method 2800 of analyzing a biological sampleof an organism to determine a classification of a level of canceraccording to embodiments of the present invention. The biological sampleincludes DNA originating from normal cells and may potentially includeDNA from cells associated with cancer. At least some of the DNA may becell-free in the biological sample.

At block 2810, a plurality of DNA molecules from the biological sampleare analyzed. The analysis of a DNA molecule can include determining alocation of the DNA molecule in a genome of the organism and determiningwhether the DNA molecule is methylated at one or more sites. Theanalysis can be performed by receiving sequence reads from amethylation-aware sequencing, and thus the analysis can be performedjust on data previously obtained from the DNA. In other embodiments, theanalysis can include the actual sequencing or other active steps ofobtaining the data.

At block 2820, a respective number of DNA molecules that are methylatedat the site is determined for each of a plurality of sites. In oneembodiment, the sites are CpG sites, and may be only certain CpG sites,as selected using one or more criteria mentioned herein. The number ofDNA molecules that are methylated is equivalent to determining thenumber that are unmethylated once normalization is performed using atotal number of DNA molecules analyzed at a particular site, e.g., atotal number of sequence reads. For example, an increase in the CpGmethylation density of a region is equivalent to a decrease in thedensity of unmethylated CpGs of the same region.

At block 2830, a first methylation level is calculated based on therespective numbers of DNA molecules methylated at the plurality ofsites. The first methylation level can correspond to a methylationdensity that is determined based on the number of DNA moleculescorresponding to the plurality of sites. The sites can correspond to aplurality of loci or just one locus.

At block 2840, the first methylation level is compared to a first cutoffvalue. The first cutoff value may be a reference methylation level or berelated to a reference methylation level (e.g., a specified distancefrom a normal level). The reference methylation level may be determinedfrom samples of individuals without cancer or from loci or the organismthat are known to not be associated with a cancer of the organism. Thefirst cutoff value may be established from a reference methylation leveldetermined from a previous biological sample of the organism obtainedprevious to the biological sample being tested.

In one embodiment, the first cutoff value is a specified distance (e.g.,a specified number of standard deviations) from a reference methylationlevel established from a biological sample obtained from a healthyorganism. The comparison can be performed by determining a differencebetween the first methylation level and a reference methylation level,and then comparing the difference to a threshold corresponding to thefirst cutoff value (e.g., to determine if the methylation level isstatistically different than the reference methylation level).

At block 2850, a classification of a level of cancer is determined basedon the comparison. Examples of a level of cancer includes whether thesubject has cancer or a premalignant condition, or an increasedlikelihood of developing cancer. In one embodiment, the first cutoffvalue may be determined from a previously obtained sample from thesubject (e.g., a reference methylation level may be determined from theprevious sample).

In some embodiments, the first methylation level can correspond to anumber of regions whose methylation levels exceed a threshold value. Forexample, a plurality of regions of a genome of the organism can beidentified. The regions can be identified using criteria mentionedherein, e.g., of certain lengths or certain number of sites. One or moresites (e.g., CpG sites) can be identified within each of the regions. Aregion methylation level can be calculated for each region. The firstmethylation level is for a first region. Each of the region methylationlevels is compared to a respective region cutoff value, which may be thesame or vary among regions. The region cutoff value for the first regionis the first cutoff value. The respective region cutoff values can be aspecified amount (e.g., 0.5) from a reference methylation level, therebycounting only regions that have a significant difference from areference, which may be determined from non-cancer subjects.

A first number of regions whose region methylation level exceeds therespective region cutoff value can be determined, and compared to athreshold value to determine the classification. In one implementation,the threshold value is a percentage. Comparing the first number to athreshold value can include dividing the first number of regions by asecond number of regions (e.g., all of the regions) before comparing tothe threshold value, e.g., as part of a normalization process.

As described above, a fractional concentration of tumor DNA in thebiological sample can be used to calculate the first cutoff value. Thefractional concentration can simply be estimated to be greater than aminimum value, whereas a sample with a fractional concentration lowerthan the minimum value can be flagged, e.g., as not being suitable foranalysis. The minimum value can be determined based on an expecteddifference in methylation levels for a tumor relative to a referencemethylation level. For example, if a difference is 0.5 (e.g., as used asa cutoff value), then a certain tumor concentration would be required tobe high enough to see this difference.

Specific techniques from method 1300 can be applied for method 2800. Inmethod 1300, copy number variations can be determined for a tumor (e.g.,where the first chromosomal region of a tumor can be tested for having acopy number change relative to a second chromosomal region of thetumor). Thus, method 1300 can presume that a tumor exists. In method2800, a sample can be tested for whether there is an indication of anytumor to exist at all, regardless of any copy number characteristics.Some techniques of the two methods can be similar. However, the cutoffvalues and methylation parameters (e.g., normalized methylation levels)for method 2800 can detect a statistical difference from a referencemethylation level for non-cancer DNA as opposed to a difference from areference methylation level for a mixture of cancer DNA and non-cancerDNA with some regions possibly having copy number variations. Thus, thereference values for method 2800 can be determined from samples withoutcancer, such as from organisms without cancer or from non-cancer tissueof the same patient (e.g., plasma taken previously or fromcontemporaneously acquired samples that are known to not have cancer,which may be determined from cellular DNA).

E. Prediction of the Minimal Fractional Concentration of Tumor DNA to beDetected Using Plasma DNA Methylation Analysis

One way to measure the sensitivity of the approach to detect cancerusing the methylation level of plasma DNA is related to the minimalfractional tumor-derived DNA concentration that is required to reveal achange in plasma DNA methylation level when compared with those ofcontrols. The test sensitivity is also dependent on the extent ofdifference in DNA methylation between the tumor tissue and baselineplasma DNA methylation levels in healthy controls or blood cell DNA.Blood cells are the predominant source of DNA in plasma of healthyindividuals. The larger the difference, the easier the cancer patientscan be discriminated from the non-cancer individuals and would bereflected as a lower detection limit of tumor-derived in plasma and ahigher clinical sensitivity in detecting the cancer patients. Inaddition, the variations in the plasma DNA methylation in the healthysubjects or in subjects with different ages (G Hannum et al. 2013 MolCell; 49: 359-367) would also affect the sensitivity of detecting themethylation changes associated with the presence of a cancer. A smallervariation in the plasma DNA methylation in the healthy subjects wouldmake the detection of the change caused by the presence of a smallamount of cancer-derived DNA easier.

FIG. 29A is a plot 2900 showing the distribution of the methylationdensities in reference subjects assuming that this distribution followsa normal distribution. This analysis is based on each plasma sample onlyproviding one methylation density value, for example, the methylationdensity of all autosomes or of a particular chromosome. It illustrateshow the specificity of the analysis would be affected. In oneembodiment, a cutoff of 3 SDs below the mean DNA methylation density ofthe reference subjects is used to determine if a tested sample issignificantly more hypomethylated than samples from the referencesubjects. When this cutoff is used, it is expected that approximately0.15% of non-cancer subjects would have false-positive results of beingclassified as having cancer resulting in a specificity of 99.85%.

FIG. 29B is a plot 2950 showing the distributions of methylationdensities in reference subjects and cancer patients. The cutoff value is3 SDs below the mean of the methylation densities of the referencesubjects. If the mean of methylation densities of the cancer patients is2 SDs below the cutoff value (i.e. 5 SDs below the mean of the referencesubjects), 97.5% of the cancer subjects would be expected to have amethylation density below the cutoff value. In other words, the expectedsensitivity would be 97.5% if one methylation density value is providedfor each subject, for example when the total methylation density of thewhole genome, of all autosomes or a particular chromosome is analyzed.The difference between the mean methylation densities of the twopopulations is affected by two factors, namely the degree of differencein the methylation level between cancer and non-cancer tissues and thefractional concentration of tumor-derived DNA in the plasma sample. Thehigher the values of these two parameters, the higher the difference invalue of the methylation densities of these two populations would be. Inaddition, the lower is the SD of the distributions of methylationdensities of the two populations, the lesser is the overlapping of thedistributions of the methylation densities of the two populations.

Here we use a hypothetical example to illustrate this concept. Let'sassume that the methylation density of the tumor tissue is approximately0.45 and that of the plasma DNA of the healthy subjects is approximately0.7. These assumed values are similar to those obtained from our HCCpatient where the overall methylation density of the autosomes is 42.9%and the mean methylation density of the autosomes for the plasma samplesfrom healthy controls was 71.6%. Assuming that the CV of measuring theplasma DNA methylation density for the whole genome is 1%, the cutoffvalue would be 0.7×(100%−3×1%)=0.679. To achieve a sensitivity of 97.5%,the mean methylation density of the plasma DNA for the cancer patientsneed to be approximately 0.679−0.7×(2×1%)=0.665. Let f represents thefractional concentration of tumor-derived DNA in the plasma sample. Thenf can be calculated as (0.7−0.45)×f=0.7−0.665. Therefore, f isapproximately 14%. From this calculation, it is estimated that theminimal fractional concentration that can be detected in the plasma is14% so as to achieve a diagnostic sensitivity of 97.5% if the totalmethylation density of the whole genome is used as the diagnosticparameter.

Next we performed this analysis on the data obtained from the HCCpatient. For this illustration, only one methylation density measurementbased on the value estimated from all autosomes was made for eachsample. The mean methylation density was 71.6% among the plasma samplesobtained from the healthy subjects. The SD of the methylation densitiesof these four samples was 0.631%. Therefore, the cutoff value for plasmamethylation density would need to be 71.6%−3×0.631%=69.7% to reach az-score <−3 and a specificity of 99.85%. To achieve a sensitivity of a97.5%, the mean plasma methylation density of the cancer patients wouldneed to be 2 SDs below the cutoff, i.e. 68.4%. Since the methylationdensity of the tumor tissue was 42.9% and using the formula:P=BKG×(1−f)+TUM×f, f would need to be at least 11.1%.

In another embodiment, the methylation densities of different genomicregions can be analyzed separately, e.g., as shown in FIG. 25A or 26B.In other words, multiple measurements of the methylation level were madefor each sample. As shown below, significant hypomethylation could bedetected at much lower fractional tumor DNA concentration in plasma andthus the diagnostic performance of the plasma DNA methylation analysisfor cancer detection would be enhanced. The number of genomic regionsshowing a significant deviation in methylation densities from thereference population can be counted. Then the number of genomic regionscan be compared to a cutoff value to determine if there is an overallsignificant hypomethylation of plasma DNA across the population ofgenomic regions surveyed, for example, the 1 Mb bins of the wholegenome. The cutoff value can be established by the analysis of a groupof reference subjects without a cancer or derived mathematically, forexample, according to normal distribution function.

FIG. 30 is a plot 3000 showing the distribution of methylation densitiesof the plasma DNA of healthy subjects and cancer patients. Themethylation density of each 1 Mb bin is compared with the correspondingvalues of the reference group. The percentage of bins showingsignificant hypomethylation (3 SDs below the mean of the referencegroup) was determined. A cutoff of 10% being significantlyhypomethylated was used to determine if tumor-derived DNA is present inthe plasma sample. Other cutoff values such as 5%, 15%, 20%, 25%, 30%,35%, 40%, 45%, 50%, 60%, 70%, 80% or 90% can also be used according tothe desired sensitivity and specificity of the test.

For example, to classify a sample as containing tumor-derived DNA, wecan use 10% of the 1 Mb bins showing significant hypomethylation(z-score <−3) as a cutoff. If there are more than 10% of the bins beingsignificantly more hypomethylated than the reference group, then thesample is classified as positive for the cancer test. For each 1 Mb bin,a cutoff of 3 SDs below the mean methylation density of the referencegroup is used to define a sample as significantly more hypomethylated.For each of the 1 Mb bins, if the mean plasma DNA methylation density ofthe cancer patients is 1.72 SDs lower than the mean plasma DNAmethylation densities of the reference subjects, then there is a 10%chance that the methylation density value of any particular bin of acancer patient would be lower than the cutoff (i.e. z-score <−3) andgives a positive result. Then, if we look at all the 1 Mb bins for thewhole genome, then approximately 10% of the bins would be expected toshow positive results of having significantly lower methylationdensities (i.e. z-scores <−3). Assuming that the overall methylationdensity of the plasma DNA of the healthy subjects is approximately 0.7and the coefficient of variation (CV) of measuring the plasma DNAmethylation density for each 1 Mb bin is 1%, the mean methylationdensity of the plasma DNA of the cancer patients would need to be0.7×(100%−1.72×1%)=0.68796. Let f be the fractional concentration oftumor-derived DNA in plasma so as to achieve this mean plasma DNAmethylation density. Assuming that the methylation density of the tumortissue is 0.45, then f can be calculated using the equation

( M _(P) _(ref) −M _(tumor))×f=M _(P) _(ref) −M _(P) _(cancer)

where M _(P) _(ref) represents the mean methylation density of plasmaDNA in the reference individuals; M_(tumor) represents the methylationdensity of the tumor tissue in the cancer patient; and M _(P) _(cancer)represents the mean methylation density of plasma DNA in the cancerpatients.

Using this equation, (0.7−0.45)×f=0.7−0.68796. Thus, the minimalfractional concentration can be detected using this approach would bededuced as 4.8%. The sensitivity can be further enhanced by decreasingthe cutoff percentage of bins being significantly more hypomethylated,for example, from 10% to 5%.

As shown in the above example, the sensitivity of this method isdetermined by the degree of difference in methylation level betweencancer and non-cancer tissues, for example, blood cells. In oneembodiment, only the chromosomal regions which show a large differencein methylation densities between the plasma DNA of the non-cancersubjects and the tumor tissue are selected. In one embodiment, onlyregions with a difference in methylation density of >0.5 are selected.In other embodiments a difference of 0.4, 0.6, 0.7, 0.8 or 0.9 can beused for selecting the suitable regions. In yet another embodiments, thephysical size of the genomic regions is not fixed. Instead, the genomicregions are defined, for example, based on a fixed read depth or a fixednumber of CpG sites. The methylation levels at a multiple of thesegenomic regions are assessed for each sample.

FIG. 31 is a graph 3100 showing the distribution of the differences inmethylation densities between the mean of the plasma DNA of healthysubjects and the tumor tissue of the HCC patient. A positive valuesignifies that the methylation density is higher in the plasma DNA ofthe healthy subjects and a negative value signifies that the methylationdensity is higher in the tumor tissue.

In one embodiment, the bins with the greatest difference between themethylation density of the cancer and non-cancer tissues can beselected, for example, those with a difference of >0.5, regardless ofwhether the tumor is hypomethylated or hypermethylated for these bins.The detection limit of fractional concentration of tumor-derived DNA inplasma can be lowered by focusing on these bins because of the greaterdifferences between the distributions of the plasma DNA methylationlevels between cancer and non-cancer subjects given the same fractionalconcentration of tumor-derived DNA in the plasma. For example, if onlybins with differences >0.5 are used and a cutoff of 10% of the binsbeing significantly more hypomethylated is adopted to determine if atested individual has a cancer, the minimal fractional concentration (f)of tumor derived DNA detected can be calculated using the followingequation: (M _(P) _(ref) −M_(tumor))×f=M _(P) _(ref) −M _(P) _(cancer) ,where M _(P) _(ref) represents the mean methylation density of plasmaDNA in the reference individuals; M_(tumor) represents the methylationdensity of the tumor tissue in the cancer patient; and M _(P) _(cancer)represents the mean methylation density of plasma DNA in the cancerpatients.

While the difference in methylation density between the plasma of thereference subjects and the tumor tissues is at least 0.5. Then, we have0.5×f=0.7−0.68796 and f=2.4%. Therefore, by focusing on bins with ahigher difference in methylation density between cancer and non-cancertissues, the lower limit of fractional tumor-derived DNA can be loweredfrom 4.8% to 2.4%. The information regarding which bins would showlarger degrees of methylation differences between cancer and non-cancertissues, for example, blood cells, could be determined from tumortissues of the same organ or same histological type obtained from otherindividuals.

In another embodiment, a parameter can be derived from the methylationdensity of the plasma DNA of all bins and taking into account thedifference in methylation densities between cancer and non-cancertissues. Bins with greater difference can be given a heavier weight. Inone embodiment, the difference in methylation density between cancer andnon-cancer tissue of each bin can directly be used as the weight if theparticular bin in calculating the final parameter.

In yet another embodiment, different types of cancer may have differentpatterns of methylation in the tumor tissue. A cancer-specific weightprofile can be derived from the degree of methylation of the specifictype of cancer.

In yet another embodiment, the inter-bin relationship of methylationdensity can be determined in subjects with and without cancer. In FIG.8, we can observe that in a small number of bins, the tumor tissues weremore methylated than the plasma DNA of the reference subjects. Thus, thebins with the most extreme values of difference, e.g. difference >0.5and difference <0, can be selected. The ratio of the methylation densityof these bins can then be used to indicate if the tested individual hascancer. In other embodiments, the difference and quotient of themethylation density of different bins can be used as parameters forindicating the inter-bin relationship.

We further assessed the detection sensitivity of the approach to detector assess tumor using the methylation densities of multiple genomicregions as illustrated by the data obtained from the HCC patient. First,we mixed reads from the pre-operative plasma with those obtained fromthe plasma samples of the healthy controls to simulate plasma samplesthat contained fractional tumor DNA concentration that ranged from 20%to 0.5%. We then scored the percentage of 1 Mb bins (out of 2,734 binsin the whole genome) with methylation densities equivalent to z-scores<−3. When the fractional tumor DNA concentration in plasma was 20%,80.0% of the bins showed significant hypomethylation. The correspondingdata for fractional tumor DNA concentration in plasma of 10%, 5%, 2%, 1%and 0.5% were 67.6%, 49.7%, 18.9%, 3.8% and 0.77% of the bins showinghypomethylation, respectively. Since the theoretical limit of the numberof bins showing z-scores <−3 in the control samples is 0.15%, our datashow that there were still more bins (0.77%) beyond the theoreticalcutoff limit even when the tumor fractional concentration was just 0.5%.

FIG. 32A is a table 3200 showing the effect of reducing the sequencingdepth when the plasma sample contained 5% or 2% tumor DNA. A highproportion of bins (>0.15%) showing significant hypomethylation couldstill be detected when the mean sequencing depth was just 0.022 timesthe haploid genome.

FIG. 32B is a graph 3250 showing the methylation densities of the repeatelements and non-repeat regions in the plasma of the four healthycontrol subjects, the buffy coat, the normal liver tissue, the tumortissue, the pre-operative plasma and the post-operative plasma samplesof the HCC patient. It can be observed that the repeat elements weremore methylated (higher methylation density) than the non-repeat regionsin both cancer and non-cancer tissues. However, the difference inmethylation between repeat elements and non-repeat regions was bigger inthe non-cancer tissues and the plasma DNA of the healthy subjects whencompared with the tumor tissues.

As a result, the plasma DNA of the cancer patient had a larger reductionin methylation density at the repeat elements than in the non-repeatregions. The difference in plasma DNA methylation density between themean of the four healthy controls and the HCC patient was 0.163 and0.088 for the repeat elements and the non-repeat regions, respectively.The data on the pre-operative and post-operative plasma samples alsoshowed that the dynamic range in the change in methylation density waslarger in the repeat than the non-repeat regions. In one embodiment, theplasma DNA methylation density of the repeat elements can be used fordetermining if a patient is affected by cancer or for the monitoring ofdisease progression.

As discussed above, the variation in methylation densities in the plasmaof the reference subjects would also affect the accuracy ofdifferentiating cancer patients from non-cancer individuals. The tighterthe distribution of methylation densities (i.e. smaller standarddeviation), the more accurate it would be to differentiate cancer andnon-cancer subjects. In another embodiment, the coefficient of variation(CV) of the methylation densities of the 1 Mb bins can be used as acriterion for selecting the bins with low variability of plasma DNAmethylation densities in the reference group. For example, only binswith CV<1% are selected. Other values, for example 0.5%, 0.75%, 1.25%and 1.5% can also be used as criteria for selecting the bins with lowvariability in methylation density. In yet another embodiment, theselection criteria can include both the CV of the bin and the differencein methylation density between cancer and non-cancer tissues.

The methylation density can also be used to estimate the fractionalconcentration of tumor-derived DNA in a plasma sample when themethylation density of the tumor tissue is known. This information canbe obtained by the analysis of the tumor of the patient or from thesurvey of the tumors from a number of patients having the same type ofcancer. As discussed above, the plasma methylation density (P) can beexpressed using the following equation: P=BKG×(1−f)+TUM×f where BKG isthe background methylation density from the blood cells and otherorgans, TUM is the methylation density in the tumor tissue, and f is thefractional concentration of tumor-derived DNA in the plasma sample. Thiscan be rewritten as:

${f = \frac{{BKG} - P}{{BKG} - {TUM}}}.$

The values of BKG can be determined by analyzing the patient's plasmasample at a time point that the cancer is not present or from the surveyof a reference group of individuals without cancer. Therefore, aftermeasuring the plasma methylation density, f can be determined.

F. Combination with Other Methods

Methylation analysis approaches described herein can be used incombination with other methods that are based on the genetic changes oftumor-derived DNA in plasma. Examples of such methods include theanalysis for cancer-associated chromosomal aberrations (K C A Chan etal. 2013 Clin Chem; 59:211-224; R J Leary et al. 2012 Sci Transl Med;4:162ra154) and cancer-associated single nucleotide variations in plasma(K C A Chan et al. 2013 Clin Chem; 59:211-224). There are advantages ofthe methylation analysis approach over those genetic approaches.

As shown in FIG. 21A, the hypomethylation of the tumor DNA is a globalphenomenon involving regions distributed across almost the entiregenome. Therefore, the DNA fragments from all chromosomal regions wouldbe informative regarding the potential contribution of the tumor-derivedhypomethylated DNA to the plasma/serum DNA in the patient. In contrast,chromosomal aberrations (either amplification or deletion of achromosomal region) are only present in some chromosomal regions and theDNA fragments from the regions without a chromosome aberration in thetumor tissue would not be informative in the analysis (K C A Chan et al.2013 Clin Chem; 59: 211-224). Similarly only a few thousand of singlenucleotide alterations are observed in each cancer genome (K C A Chan etal. 2013 Clin Chem; 59: 211-224). DNA fragments that do not overlap withthese single nucleotide changes would not be informative in determiningif tumor-derived DNA is present in the plasma. Therefore, thismethylation analysis approach is potentially more cost-effective thanthose genetic approaches for detecting cancer-associated changes in thecirculation.

In one embodiment, the cost-effectiveness of plasma DNA methylationanalysis can further be enhanced by enriching for DNA fragments from themost informative regions, for example regions with highest differentialmethylation difference between cancer and non-cancer tissues. Examplesfor the methods of enriching for these regions include the use ofhybridization probes (e.g. Nimblegen SeqCap system and AgilentSureSelect Target Enrichment system), PCR amplification and solid phasehybridization.

G. Tissue-Specific Analysis/Donors

Tumor-derived cells invade and metastasize to adjacent or distantorgans. The invaded tissues or metastatic foci contribute DNA intoplasma as a result of cell death. By analyzing the methylation profileof DNA in the plasma of cancer patients and detecting the presence oftissue-specific methylation signatures, one could detect the types oftissues that are involved in the disease process. This approach providesa noninvasive anatomic scan of the tissues involved in the cancerousprocess to aid in the identification of the organs involved as theprimary and metastatic sites. Monitoring the relative concentrations ofthe methylation signatures of the involved organs in plasma would alsoallow one to assess the tumor burden of those organs and determine ifthe cancer process in that organ is deteriorating or improving or hadbeen cured. For example, if a gene X is specifically methylated in theliver. Then, metastatic involvement of the liver by a cancer (e.g.colorectal cancer) will be expected to increase the concentration ofmethylated sequences from gene X in the plasma. There would also beanother sequence or groups of sequences with similar methylationcharacteristics as gene X. One could then combine the results from suchsequences. Similar considerations are applicable to other tissues, e.g.the brain, bones, lungs and kidneys, etc.

On the other hand, DNA from different organs is known to exhibittissue-specific methylation signatures (B W Futscher et al. 2002 NatGenet; 31:175-179; S S C Chim et al. 2008 Clin Chem; 54: 500-511). Thus,methylation profiling in plasma can be used for elucidating thecontribution of tissues from various organs into plasma. The elucidationof such contribution can be used for assessing organ damage, as plasmaDNA is believed to be released when cells die. For example, liverpathology such as hepatitis (e.g. by viruses, autoimmune processes, etc)or hepatoxicity (e.g. drug overdose (such as by paracetamol) or toxins(such as alcohol) caused by drugs is associated with liver cell damageand will be expected to be associated with increased level ofliver-derived DNA in plasma. For example, if a gene X is specificallymethylated in the liver. Then, liver pathology will be expected toincrease the concentration of methylated sequences from gene X in theplasma. Conversely, if a gene Y is specifically hypomethylated in theliver. Then, liver pathology will be expected to decrease theconcentration of methylated sequences from gene Y in the plasma. In yetother embodiment, gene X or Y can be replaced by any genomic sequencesthat may not be a gene and that exhibit differential methylation indifferent tissues within the body.

Techniques described herein could also be applied to the assessment ofdonor-derived DNA in the plasma of organ transplantation recipients (Y MD Lo et al. 1998 Lancet; 351:1329-1330). Polymorphic differences betweenthe donor and recipient had been used to distinguish the donor-derivedDNA from the recipient-derived DNA in plasma (Y W Zheng et al. 2012 ClinChem; 58: 549-558). We propose that tissue-specific methylationsignatures of the transplanted organ could also be used as a method todetect the donor's DNA in the recipient's plasma.

By monitoring the concentration of the donor's DNA, one couldnoninvasively assess the status of the transplanted organ. For example,transplant rejection is associated with higher rate of cell death andhence the concentration of the donor's DNA in the recipient's plasma (orserum), as reflected by the methylation signature of the transplantedorgan, would be increased when compared with the time when the patientis in stable condition or when compared to other stable transplantrecipients or healthy controls without transplantation. Similar to whathas been described for cancer, the donor-derived DNA could be identifiedin the plasma of transplantation recipients by detecting for all or someof the characteristic features, including polymorphic differences,shorter size DNA for the transplanted solid organs (Y W Zheng et al.2012 Clin Chem; 58: 549-558) and tissue-specific methylation profile.

H. Normalizing Methylation Based on Size

As described above and by Lun et al (F M F Lun et al. Clin. Chem. 2013;doi:10.1373/clinchem.2013.212274), the methylation density (e.g., ofplasma DNA) is correlated with the size of the DNA fragments. Thedistribution of methylation densities for shorter plasma DNA fragmentswas significantly lower than that for longer fragments. We propose thatsome non-cancer conditions (e.g., systemic lupus erythematosus (SLE))with abnormal fragmentation patterns of plasma DNA may exhibit anapparent hypomethylation of plasma DNA due to the presence of moreabundant short plasma DNA fragments, which are less methylated. In otherwords, the size distribution of plasma DNA can be a confounding factorfor the methylation density for plasma DNA.

FIG. 34A shows a size distribution of plasma DNA in the SLE patientSLE04. The size distributions of nine healthy control subjects are shownas dotted grey lines and that for SLE04 is shown as a black solid line.Short plasma DNA fragments were more abundant in SLE04 than in the ninehealthy control subjects. As shorter DNA fragments are generally lessmethylated, this size distribution pattern may confound the methylationanalysis for plasma DNA and lead to more apparent hypomethylation.

In some embodiments, a measured methylation level can be normalized toreduce the confounding effect of size distribution on plasma DNAmethylation analysis. For example, a size of DNA molecules at theplurality of sites can be measured. In various implementations, themeasurement can provide a specific size (e.g., length) to a DNA moleculeor simply determine that the size falls within a specific range, whichcan also correspond to a size. The normalized methylation level can thenbe compared to a cutoff value. There are several ways to perform thenormalization to reduce the confounding effect of size distribution onplasma DNA methylation analysis.

In one embodiment, size fractionation of DNA (e.g., plasma DNA) can beperformed. The size fractionation can ensure that DNA fragments of asimilar size are used to determine the methylation level in a mannerconsistent with the cutoff value. As part of the size fractionation, DNAfragments having a first size (e.g., a first range of lengths) can beselected, where the first cutoff value corresponds to the first size.The normalization can be achieved by calculating the methylation levelusing only the selected DNA fragments.

The size fractionation can be achieved in various ways, e.g., either byphysical separation of different sized DNA molecules (e.g. byelectrophoresis or microfluidics-based technologies, orcentrifugation-based technologies) or by in silico analysis. For insilico analysis, in one embodiment, one can perform paired-end massivelyparallel sequencing of the plasma DNA molecules. One can then deduce thesize of the sequenced molecules by comparison with the location of eachof two ends of a plasma DNA molecule to a reference human genome. Then,one can perform subsequent analysis by the selection of sequenced DNAmolecules that match one or more size selection criteria (e.g., thecriteria of the size being within a specified range). Thus, in oneembodiment, the methylation density can be analyzed for fragments with asimilar size (e.g., within a specified range). The cutoff value (e.g.,in block 2840 of method 2800) can be determined based on fragmentswithin the same size range. For instance, methylation levels can bedetermined from samples that are known to have cancer or not havecancer, and the cutoff values can be determined from these methylationlevels.

In another embodiment, a functional relationship between methylationdensity and size of circulating DNA can be determined. The functionalrelationship can be defined by data point or coefficients of a function.The functional relationship can provide scaling values corresponding torespective sizes (e.g., shorter sizes can have corresponding increasesto the methylation). In various implementations, the scaling value canbe between 0 and 1 or greater than 1.

The normalization can be made based on an average size. For example, anaverage size corresponding to DNA molecules used to calculate the firstmethylation level can be computed, and the first methylation level canbe multiplied by the corresponding scaling value (i.e., corresponding tothe average size). As another example, the methylation density of eachDNA molecule can be normalized according to the size of the DNA moleculeand relationship between DNA size and methylation.

In another implementation, the normalization can be done on a permolecule basis. For example, a respective size of a DNA molecule at aparticular site can be obtained (e.g., as described above), and ascaling value corresponding to the respective size can be identifiedfrom the functional relationship. For a non-normalized calculation, eachmolecule would be counted equally in determining a methylation index atthe site. For the normalized calculation, the contribution of a moleculeto the methylation index can be weighted by the scaling factor thatcorresponds to the size of the molecule.

FIGS. 34B and 34C show methylation analysis for plasma DNA from a SLEpatient SLE04 (FIG. 34B) and a HCC patient TBR36 (FIG. 34C). The outercircles show the Z_(meth) results for plasma DNA without in silico sizefractionation. The inner circles show the Z_(meth) results for plasmaDNA of 130 bp or longer. For the SLE patient SLE04, 84% of the binsshowed hypomethylation without in silico size fractionation. Thepercentage of the bins showing hypomethylation was reduced to 15% whenonly fragments of 130 bp or longer were analyzed. For the HCC patientTBR36, 98.5% and 98.6% of bins showed hypomethylation for plasma DNAwith and without in silico size fractionation, respectively. Theseresults suggest that in silico size fractionation can effectively reducethe false-positive hypomethylation results related to increasedfragmentation of plasma DNA, e.g., in patients with SLE or in otherinflammatory conditions.

In one embodiment, the results for the analyses with and without sizefractionation can be compared to indicate if there is any confoundingeffect of size on the methylation results. Thus, in addition or insteadof normalization, the calculation of a methylation level at a particularsize can be used to determine whether there is a likelihood of a falsepositive when the percentage of bins above a cutoff value differs withand without size fractionation, or whether just a particular methylationlevel differs. For example, the presence of a significant differencebetween the results for samples with and without size fractionation canbe used to indicate the possibility of a false-positive result due to anabnormal fragmentation pattern. The threshold for determining if thedifference is significant can be established via the analysis of acohort of cancer patients and a cohort of non-cancer control subjects.

I. Analysis for Genomewide CpG Islands Hypermethylation in Plasma

In addition to general hypomethylation, hypermethylation of CpG islandsis also commonly observed in cancers (S B Baylin et al. 2011 Nat RevCancer; 11: 726-734; P A Jones et al. 2007, Cell; 128: 683-692; MEsteller et al. 2007 Nat Rev Genet 2007; 8: 286-298; M Ehrlich et al.2002 Oncogene 2002; 21: 5400-5413). In this section, we describe the useof genomewide analysis for CpG island hypermethylation for the detectionand monitoring of cancers.

FIG. 35 is a flowchart of a method 3500 determining a classification ofa level of cancer based on hypermethylation of CpG islands according toembodiments of the present invention. The plurality of sites of method2800 can include CpG sites, wherein the CpG sites are organized into aplurality of CpG islands, each CpG island including one or more CpGsites. Methylation levels for each CpG island can be used to determinethe classification of the level of cancer.

At block 3510, CpG islands to be analyzed are identified. In thisanalysis, as an example, we first defined a set of CpG islands to beanalyzed, which are characterized with relatively low methylationdensities in the plasma of the healthy reference subjects. In oneaspect, the variation of the methylation densities in the referencegroup can be relatively small so as to allow detection ofcancer-associated hypermethylation more easily. In one embodiment, theCpG islands have a mean methylation density of less than a firstpercentage in a reference group, and a coefficient of variation for themethylation density in the reference group is less than a secondpercentage.

As an example, for illustration purpose, the following criteria are usedfor the identification of the useful CpG islands:

i. The mean methylation density for the CpG island in the referencegroup (e.g. healthy subjects)<5%

ii. The coefficient of variation for the analysis of methylation densityin plasma for the reference group (e.g. healthy subjects)<30%.

These parameters can be adjusted for a specific application. From ourdataset, 454 CpG islands in the genome fulfilled these criteria.

At block 3520, the methylation density is calculated for each CpGisland. The methylation densities can be determined, as describedherein.

At block 3530, it is determined whether each of the CpG islands ishypermethylated. For example, for the analysis for CpG islandhypermethylation of a tested case, the methylation density of each CpGisland was compared with corresponding data of a reference group. Themethylation density (an example of a methylation level) can be comparedto one or more cutoff values to determine whether a particular island ishypermethylated.

In one embodiment, a first cutoff value can correspond to a mean ofmethylation densities for the reference group plus a specifiedpercentage. Another cutoff value can correspond to the mean ofmethylation densities for the reference group plus a specified number ofstandard deviations. In one implementation, a z-score (Z_(meth)) wascalculated and compared to cutoff values. As an example, a CpG island ina test subject (e.g. a subject being screened for cancer) was regardedas significantly hypermethylated if it fulfilled the following criteria:

i. its methylation density was higher than the mean of the referencegroup by 2%, and

ii. Z_(meth)>3

These parameters can also be adjusted for a specific application.

At block 3540, the methylation densities (e.g., as z-scores) of thehypermethylated CpG islands are used to determine a cumulative score.For example, after the identification of all the significantlyhypermethylated CpG islands, a score involving a sum of z-scores orfunctions of z-scores of all of the hypermethylated CpG islands can becalculated. An example of a score is a cumulative probability (CP)score, as is described in another section. The cumulative probabilityscore uses Z_(meth) to determine the probability of having such anobservation by chance according to a probability distribution (e.g.,Student's t probability distribution with 3 degree of freedom).

At block 3550, the cumulative score is compared to a cumulativethreshold to determine a classification of a level of cancer. Forexample, if the total hypermethylation in the identified CpG islands islarge enough, then the organism can be identified as having cancer. Inone embodiment, the cumulative threshold corresponds to a highestcumulative score from the reference group.

IX. Methylation and CNA

As mentioned above, methylation analysis approaches described herein canbe used in combination with other methods that are based on the geneticchanges of tumor-derived DNA in plasma. Examples of such methods includethe analysis for cancer-associated chromosomal aberrations (K C A Chanet al. 2013 Clin Chem; 59: 211-224; R J Leary et al. 2012 Sci TranslMed; 4: 162ra154). Aspects of copy number aberrations (CNA) aredescribed in U.S. patent application Ser. No. 13/308,473.

A. CNA

Copy number aberrations can be detected by counting DNA fragments thatalign to a particular part of the genome, normalizing the count, andcomparing the count to a cutoff value. In various embodiments, thenormalization can be performed by a count of DNA fragments aligned toanother haplotype of the same part of the genome (relative haplotypedosage (RHDO)) or by a count of DNA fragments aligned to another part ofthe genome.

The RHDO method relies on using heterozygous loci. Embodiments describedin this section can also be used for loci that are homozygous bycomparing two regions and not two haplotypes of the same region, andthus are non-haplotype specific. In a relative chromosomal region dosagemethod, the number of fragments from one chromosomal region (e.g., asdetermined by counting the sequence reads aligned to that region) iscompared to an expected value (which may be from a reference chromosomeregion or from the same region in another sample that is known to behealthy). In this manner, a fragment would be counted for a chromosomalregion regardless of which haplotype the sequenced tag is from. Thus,sequence reads that contain no heterozygous loci could still be used. Toperform the comparison, an embodiment can normalize the tag count beforethe comparison. Each region is defined by at least two loci (which areseparated from each other), and fragments at these loci can be used toobtain a collective value about the region.

A normalized value for the sequenced reads (tags) for a particularregion can be calculated by dividing the number of sequenced readsaligning to that region by the total number of sequenced reads alignableto the whole genome. This normalized tag count allows results from onesample to be compared to the results of another sample. For example, thenormalized value can be the proportion (e.g., percentage or fraction) ofsequenced reads expected to be from the particular region, as is statedabove. In other embodiments, other methods for normalization arepossible. For example, one can normalize by dividing the number ofcounts for one region by the number of counts for a reference region (inthe case above, the reference region is just the whole genome). Thisnormalized tag count can then be compared against a threshold value,which may be determined from one or more reference samples notexhibiting cancer.

The normalized tag count of the tested case would then be compared withthe normalized tag count of one or more reference subjects, e.g. thosewithout cancer. In one embodiment, the comparison is made by calculatingthe z-score of the case for the particular chromosomal region. Thez-score can be calculated using the following equation:z-score=(normalized tag count of the case−mean)/SD, where “mean” is themean normalized tag count aligning to the particular chromosomal regionfor the reference samples; and SD is the standard deviation of thenumber of normalized tag count aligning to the particular region for thereference samples. Hence, the z-score is the number of standarddeviation that the normalized tag count of a chromosomal region for thetested case is away from the mean normalized tag count for the samechromosomal region of the one or more reference subjects.

In the situation when the tested organism has cancer, the chromosomalregions that are amplified in the tumor tissues would beover-represented in the plasma DNA. This would result in a positivevalue of the z-score. On the other hand, chromosomal regions that aredeleted in the tumor tissues would be under-represented in the plasmaDNA. This would result in a negative value of the z-score. The magnitudeof the z-score is determined by several factors.

One factor is the fractional concentration of tumor-derived DNA in thebiological sample (e.g. plasma). The higher the fractional concentrationof tumor-derived DNA in the sample (e.g. plasma), the larger thedifference between the normalized tag count of the tested case and thereference cases would be. Hence, a larger magnitude of the z-score wouldresult.

Another factor is the variation of the normalized tag count in the oneor more reference cases. With the same degree of the over-representationof the chromosomal region in the biological sample (e.g. plasma) of thetested case, a smaller variation (i.e. a smaller standard deviation) ofthe normalized tag count in the reference group would result in a higherz-score. Similarly, with the same degree of under-representation of thechromosomal region in the biological sample (e.g. plasma) of the testedcase, a smaller standard deviation of the normalized tag count in thereference group would result in a more negative z-score.

Another factor is the magnitude of chromosomal aberration in the tumortissues. The magnitude of chromosomal aberration refers to the copynumber changes for the particular chromosomal region (either gain orloss). The higher the copy number changes in the tumor tissues, thehigher the degree of over- or under-representation of the particularchromosomal region in the plasma DNA. For example, the loss of bothcopies of the chromosome would result in greater under-representation ofthe chromosomal region in the plasma DNA than the loss of one of the twocopies of the chromosome and, hence, resulted in a more negativez-score. Typically, there are multiple chromosomal aberrations incancers. The chromosomal aberrations in each cancer can further vary byits nature (i.e. amplification or deletion), its degree (single ormultiple copy gain or loss) and its extent (size of the aberration interms of chromosomal length).

The precision of measuring the normalized tag count is affected by thenumber of molecules analyzed. We expect that 15,000, 60,000 and 240,000molecules would need to be analyzed to detect chromosomal aberrationswith one copy change (either gain or loss) when the fractionalconcentration is approximately 12.5%, 6.3% and 3.2% respectively.Further details of the tag counting for detection of cancer fordifferent chromosomal regions is described in U.S. Patent PublicationNo. 2009/0029377 entitled “Diagnosing Fetal Chromosomal Aneuploidy UsingMassively Parallel Genomic Sequencing” by Lo et al., the entire contentsof which are herein incorporated by reference for all purposes.

Embodiments can also use size analysis, instead of the tag countingmethod. Size analysis may also be used, instead of a normalized tagcount. The size analysis can use various parameters, as mentionedherein, and in U.S. patent application Ser. No. 12/940,992. For example,the Q or F values from above may be used. Such size values do not need anormalization by counts from other regions as these values do not scalewith the number of reads. Techniques of the haplotype-specific methods,such as the RHDO method described above and in more detail in U.S.patent application Ser. No. 13/308,473, can be used for the non-specificmethods as well. For example, techniques involving the depth andrefinement of a region may be used. In some embodiments, a GC bias for aparticular region can be taken into account when comparing two regions.Since the RHDO method uses the same region, such a correction is notneeded.

Although certain cancers can typically present with aberrations inparticular chromosomal regions, such cancers do not always exclusivelypresent with aberrations in such regions. For example, additionalchromosomal regions could show aberrations, and the location of suchadditional regions may be unknown. Furthermore, when screening patientsto identify early stages of cancer, one may want to identify a broadrange of cancers, which could show aberrations present throughout thegenome. To address these situations, embodiments can analyze a pluralityof regions in a systematic fashion to determine which regions showaberrations. The number of aberrations and their location (e.g. whetherthey are contiguous) can be used, for example, to confirm aberrations,determine a stage of the cancer, provide a diagnosis of cancer (e.g. ifthe number is greater than a threshold value), and provide a prognosisbased on the number and location of various regions exhibiting anaberration.

Accordingly, embodiments can identify whether an organism has cancerbased on the number of regions that show an aberration. Thus, one cantest a plurality of regions (e.g., 3,000) to identify a number ofregions that exhibit an aberration. The regions may cover the entiregenome or just parts of the genome, e.g., non-repeat region.

FIG. 36 is a flowchart of a method 3600 of analyzing a biological sampleof an organism using a plurality of chromosomal regions according toembodiments of the present invention. The biological sample includesnucleic acid molecules (also called fragments).

At block 3610, a plurality of regions (e.g., non-overlapping regions) ofthe genome of the organism are identified. Each chromosomal regionincludes a plurality of loci. A region can be 1 Mb in size, or someother equal-size. For the situation of a region being 1 Mb in size, theentire genome can then include about 3,000 regions, each ofpredetermined size and location. Such predetermined regions can vary toaccommodate a length of a particular chromosome or a specified number ofregions to be used, and any other criteria mentioned herein. If regionshave different lengths, such lengths can be used to normalize results,e.g., as described herein. The regions can be specifically selectedbased on certain criteria of the specific organism and/or based onknowledge of the cancer being tested. The regions can also bearbitrarily selected.

At block 3620, a location of the nucleic acid molecule in a referencegenome of the organism is identified for each of a plurality of nucleicacid molecules. The location may be determined in any of the waysmentioned herein, e.g., by sequencing the fragments to obtain sequencedtags and aligning the sequenced tags to the reference genome. Aparticular haplotype of a molecule can also be determined for thehaplotype-specific methods.

Blocks 3630-3650 are performed for each of the chromosomal regions. Atblock 3630, a respective group of nucleic acid molecules is identifiedas being from the chromosomal region based on the identified locations.The respective group can include at least one nucleic acid moleculelocated at each of the plurality of loci of the chromosomal region. Inone embodiment, the group can be fragments that align to a particularhaplotype of the chromosomal region, e.g., as in the RHDO method above.In another embodiment, the group can be of any fragment that aligns tothe chromosomal region.

At block 3640, a computer system calculates a respective value of therespective group of nucleic acid molecules. The respective value definesa property of the nucleic acid molecules of the respective group. Therespective value can be any of the values mentioned herein. For example,the value can be the number of fragments in the group or a statisticalvalue of a size distribution of the fragments in the group. Therespective value can also be a normalized value, e.g., a tag count ofthe region divided by the total number of tag counts for the sample orthe number of tag counts for a reference region. The respective valuecan also be a difference or ratio from another value (e.g., in RHDO),thereby providing the property of a difference for the region.

At block 3650, the respective value is compared to a reference value todetermine a classification of whether the first chromosomal regionexhibits a deletion or an amplification. This reference value can be anythreshold or reference value described herein. For example, thereference value could be a threshold value determined for normalsamples. For RHDO, the respective value could be the difference or ratioof tag counts for the two haplotypes, and the reference value can be athreshold for determining that a statistically significant deviationexists. As another example, the reference value could be the tag countor size value for another haplotype or region, and the comparison caninclude taking a difference or ratio (or function of such) and thendetermining if the difference or ratio is greater than a thresholdvalue.

The reference value can vary based on the results of other regions. Forexample, if neighboring regions also show a deviation (although smallcompared to one threshold, e.g., a z-score of 3), then a lower thresholdcan be used. For example, if three consecutive regions are all above afirst threshold, then cancer may be more likely. Thus, this firstthreshold may be lower than another threshold that is required toidentify cancer from non-consecutive regions. Having three regions (ormore than three) having even a small deviation can have a low enoughprobability of a chance effect that the sensitivity and specificity canbe preserved.

At block 3660, an amount of genomic regions classified as exhibiting adeletion or amplification is determined. The chromosomal regions thatare counted can have restrictions. For example, only regions that arecontiguous with at least one other region may be counted (or contiguousregions can be required to be of a certain size, e.g., 4 or moreregions). For embodiments where the regions are not equal, the numbercan also account for the respective lengths (e.g., the number could be atotal length of the aberrant regions).

At block 3670, the amount is compared to an amount threshold value todetermine a classification of the sample. As examples, theclassification can be whether the organism has cancer, a stage of thecancer, and a prognosis of the cancer. In one embodiment, all aberrantregions are counted and a single threshold value is used regardless ofwhere the regions appear. In another embodiment, a threshold value canvary based on the locations and size of the regions that are counted.For example, the amount of regions on a particular chromosome or arm ofa chromosome may be compared to a threshold for that particularchromosome (or arm). Multiple thresholds may be used. For instance, theamount of aberrant regions on a particular chromosome (or arm) must begreater than a first threshold value, and the total amount of aberrantregions in the genome must be greater than a second threshold value. Thethreshold value can be a percentage of the regions that are determinedto exhibit a deletion or an amplification.

This threshold value for the amount of regions can also depend on howstrong the imbalance is for the regions counted. For example, the amountof regions that are used as the threshold for determining aclassification of cancer can depend on the specificity and sensitivity(aberrant threshold) used to detect an aberration in each region. Forexample, if the aberrant threshold is low (e.g. z-score of 2), then theamount threshold may be selected to be high (e.g., 150). But, if theaberrant threshold is high (e.g., a z-score of 3), then the amountthreshold may be lower (e.g., 50). The amount of regions showing anaberration can also be a weighted value, e.g., one region that shows ahigh imbalance can be weighted higher than a region that just shows alittle imbalance (i.e. there are more classifications than just positiveand negative for the aberration). As an example, a sum of z-scores canbe used, thereby using the weighted values.

Accordingly, the amount (which may include number and/or size) ofchromosomal regions showing significant over- or under-representation ofa normalized tag count (or other respective value for the property ofthe group) can be used for reflecting the severity of disease. Theamount of chromosomal regions with an aberrant normalized tag count canbe determined by two factors, namely the number (or size) of chromosomalaberrations in the tumor tissues and the fractional concentration oftumor-derived DNA in the biological sample (e.g. plasma). More advancedcancers tend to exhibit more (and larger) chromosomal aberrations.Hence, more cancer-associated chromosomal aberrations would potentiallybe detectable in the sample (e.g. plasma). In patients with moreadvanced cancer, the higher tumor load would lead to a higher fractionalconcentration of tumor-derived DNA in the plasma. As a result, thetumor-associated chromosomal aberrations would be more easily detectedin the plasma sample.

One possible approach for improving the sensitivity without sacrificingthe specificity is to take into account the result of the adjacentchromosomal segment. In one embodiment, the cutoff for the z-scoreremains to be >2 and <−2. However, a chromosomal region would beclassified as potentially aberrant only when two consecutive segmentswould show the same type of aberrations, e.g. both segments have az-score of >2. In other embodiments, the z-score of neighboring segmentscan be added together using a higher cutoff value. For example, thez-scores of three consecutive segments can be summed and a cutoff valueof 5 can be used. This concept can be extended to more than threeconsecutive segments.

The combination of amount and aberrant thresholds can also depend on thepurpose of the analysis, and any prior knowledge of the organism (orlack thereof). For example, if screening a normal healthy population forcancer, then one would typically use high specificity, potentially inboth the amount of regions (i.e. high threshold for the number ofregions) and an aberrant threshold for when a region is identified ashaving an aberration. But, in a patient with higher risk (e.g. a patientcomplaining of a lump or family history, smoker, chronic humanpapillomavirus (HPV) carrier, hepatitis virus carrier, or other viruscarrier) then the thresholds could be lower in order to have moresensitivity (less false negatives).

In one embodiment, if one uses a 1-Mb resolution and a lower detectionlimit of 6.3% of tumor-derived DNA for detecting a chromosomalaberration, the number of molecules in each 1-Mb segment would need tobe 60,000. This would be translated to approximately 180 million (60,000reads/Mb×3,000 Mb) alignable reads for the whole genome.

A smaller segment size would give a higher resolution for detectingsmaller chromosomal aberrations. However, this would increase therequirement of the number of molecules to be analyzed in total. A largersegment size would reduce the number of molecules required for theanalysis at the expense of resolution. Therefore, only largeraberrations can be detected. In one implementation, larger regions couldbe used, segments showing an aberration could be subdivided and thesesubregions analyzed to obtain better resolution (e.g., as is describedabove). If one has an estimate for a size of deletion or amplificationto be detected (or minimum concentration to detect), the number ofmolecules to analyze can be determined.

B. CNA based on Sequencing of Bisulfite-Treated Plasma DNA

Genomewide hypomethylation and CNA can be frequently observed in tumortissues. Here, we demonstrate that the information of CNA andcancer-associated methylation changes can be simultaneously obtainedfrom the bisulfate sequencing of plasma DNA. As the two types ofanalyses can be carried out on the same data set, virtually there is noadditional cost for the CNA analysis. Other embodiments may usedifferent procedures to obtain the methylation information and thegenetic information. In other embodiments, one can perform a similaranalysis for cancer-associated hypermethylation in conjunction with theCNA analysis.

FIG. 37A shows CNA analysis for tumor tissues, non-bisulfite(BS)-treated plasma DNA and bisulfate-treated plasma DNA (from inside tooutside) for patient TBR36. FIG. 37A shows CNA analysis for tumortissues, non-bisulfite (BS)-treated plasma DNA and bisulfite-treatedplasma DNA (from inside to outside) for patient TBR36. The outermostring shows the chromosome ideogram. Each dot represents the result of a1-Mb region. The green, red and grey dots represent regions with copynumber gain, copy number loss and no copy number change, respectively.For plasma analysis, the z-scores are shown. A difference of 5 ispresent between two concentric lines. For tumor tissue analysis, thecopy number is shown. One copy difference is present between twoconcentric lines. FIG. 38A shows CNA analysis for tumor tissues,non-bisulfite (BS)-treated plasma DNA and bisulfite-treated plasma DNA(from inside to outside) for patient TBR34. The patterns of CNA detectedin the bisulfite- and non-bisulfite-treated plasma samples wereconcordant.

The patterns of CNA detected in the tumor tissues, non-bisulfite-treatedplasma and bisulfite-treated plasma were concordant. To further evaluatethe concordance between the results of the bisulfite- andnon-bisulfite-treated plasma, a scatter plot is constructed. FIG. 37B isa scatter plot showing the relationship between the z-scores for thedetection of CNA using bisulfite- and non-bisulfite-treated plasma ofthe 1 Mb bins for the patient TBR36. A positive correlation between thez-scores of the two analyses was observed (r=0.89, p<0.001, Pearsoncorrelation). FIG. 38B is a scatter plot showing the relationshipbetween the z-scores for the detection of CNA using bisulfite-treatedand non-bisulfite-treated plasma of the 1 Mb bins for the patient TBR34.A positive correlation between the z-scores of the two analyses wasobserved (r=0.81, p<0.001, Pearson correlation).

C. Synergistic Analysis of Cancer Associated CNA and Methylation Changes

As described above, the analysis for CNA can involve the counting of thenumber of sequence reads in each 1 Mb region whereas the analysis formethylation density can involve the detection of the proportion ofcytosine residues at CpG dinucleotides being methylated. The combinationof these two analyses can give synergistic information for the detectionof cancer. For example, the methylation classification and the CNAclassification can be used to determine a third classification of alevel of cancer.

In one embodiment, the presence of either cancer-associated CNA ormethylation change can be used to indicate the potential presence of acancer. In such embodiment, the sensitivity of detecting cancer can beincreased when either CNA or methylation changes are present in theplasma of a tested subject. In another embodiment, the presence of bothchanges can be used to indicate the presence of a cancer. In suchembodiment, the specificity of the test can be improved because eitherof the two types of changes can potentially be detected in somenon-cancer subjects. Thus, the third classification can be positive forcancer only when both the first classification and the secondclassification indicate cancer.

26 HCC patients and 22 healthy subjects were recruited. A blood samplewas collected from each subject and the plasma DNA was sequenced afterbisulfate treatment. For the HCC patients, the blood samples werecollected at the time of diagnosis. The presence of significant amountsof CNA was, for example, defined as having >5% of the bins showing az-score of <−3 or >3. The presence of significant amounts ofcancer-associated hypomethylation was defined as having >3% of the binsshowing a z-score of <−3. As examples, the amount of regions (bins) canbe expressed as a raw count of bins, a percentage, and a length of thebins.

Table 3 shows detection of significant amounts of CNA and methylationchanges in the plasma of 26 HCC patients using massively parallelsequencing on bisulfate-treated plasma DNA.

TABLE 3 CNA Presence Absence Methylation Presence 12 6 change Absence 17

The detection rates of cancer-associated methylation change and CNA were69% and 50%, respectively. The detection rate (i.e. diagnosticsensitivity) improved to 73% if the presence of either criterion wasused to indicate the potential presence of a cancer.

The results of two patients showing either the presence of CNA (FIG.39A) or methylation changes (FIG. 39B) are shown. FIG. 39A is a Circosplot showing the CNA (inner ring) and methylation analysis (outer ring)for the bisulfite-treated plasma for a HCC patient TBR240. For the CNAanalysis, green, red and grey dots represent regions with chromosomalgain, loss and no copy number change, respectively. For the methylationanalysis, green, red and grey dots represent regions withhypermethylation, hypomethylation and normal methylation, respectively.In this patient, cancer-associated CNA was detected in the plasmawhereas the methylation analysis did not reveal significant amounts ofcancer-associated hypomethylation. FIG. 39B is a Circos plot showing theCNA (inner ring) and methylation analysis (outer ring) for thebisulfate-treated plasma for a HCC patient TBR164. In this patient,cancer-associated hypomethylation was detected in the plasma. However,no significant amounts of CNA could be observed. The results of twopatients showing the presence of both CNA and methylation changes areshown in FIGS. 48A (TBR36) and 49A (TBR34).

Table 4 shows detection of significant amounts of CNA and methylationchanges in the plasma of 22 control subjects using massively parallelsequencing on bisulfite-treated plasma DNA. A bootstrapping (i.e.leave-one-out) approach was used for the evaluation of each controlsubjects. Thus, when a particular subject was evaluated, the other 21subjects were used for the calculation of the mean and SD of the controlgroup.

TABLE 4 CNA Presence Absence Methylation Presence 1 2 change Absence 118

The specificity of the detection of significant amounts of methylationchange and CNA were 86% and 91%, respectively. The specificity improvedto 95% if the presence of both criteria was required to indicate thepotential presence of a cancer.

In one embodiment, samples positive for CNA and/or hypomethylation areconsidered positive for cancer, and samples when both are undetectableare considered negative. Using the “or” logic provides highersensitivity. In another embodiment, only samples that are positive forboth CNA and hypomethylation are considered positive for cancer, therebyproviding higher specificity. In yet another embodiment, three tiers ofclassification can be used. Subjects are classified into i. both normal;ii. one abnormal; iii. both abnormal.

Different follow-up strategies can be used for these threeclassifications. For example, subjects for (iii) can be subjected to themost intensive follow-up protocol, e.g. involving whole body imaging;subjects for (ii) can be subjected to a less intensive follow-upprotocol, e.g. repeat plasma DNA sequencing following a relative shorttime interval of several weeks; and subjects for (i) can be subjected tothe least intensive follow-up protocol such as retesting following anumber of years. In other embodiments, the methylation and CNAmeasurements can be used in conjunction with other clinical parameters(e.g. imaging results or serum biochemistry) for further refining theclassification.

D. Prognostic Value of the Plasma DNA Analysis after Curative-IntentTreatment

The presence of cancer-associated CNA and/or methylation changes inplasma would indicate the presence of tumor-derived DNA in thecirculation of the cancer patient. A reduction or clearance of thesecancer-associated changes would be expected after treatment (e.g.,surgery). On the other hand, the persistence of these changes in theplasma after treatment could indicate the incomplete removal of alltumor cells from the body and can be a useful prognosticator for diseaserecurrence.

Blood samples were collected from the two HCC patients TBR34 and TBR36at one week after curative-intent surgical resection of the tumors. CNAand methylation analyses were performed on the bisulfate-treatedpost-treatment plasma samples.

FIG. 40A shows CNA analysis on bisulfite-treated plasma DNA collectedbefore (inner ring) and after (outer ring) surgical resection of tumorfor HCC patient TBR36. Each dot represents the result of a 1-Mb region.The green, red and grey dots represent regions with copy number gain,copy number loss and no copy number change, respectively. Most of theCNA observed before treatment disappeared after tumor resection. Theproportion of bins showing a z-score of <−3 or >3 decreased from 25% to6.6%.

FIG. 40B shows methylation analysis on bisulfate-treated plasma DNAcollected before (inner ring) and after (outer ring) surgical resectionof tumor for HCC patient TBR36. The green, red and grey dots representregions with hypermethylation, hypomethylation and normal methylation,respectively. There was a marked reduction in the proportion of binsshowing significant hypomethylation from 90% to 7.9% and the degree ofhypomethylation also showed a marked reduction. This patient had acomplete clinical remission at 22 months after tumor resection.

FIG. 41A shows CNA analysis on bisulfite-treated plasma DNA collectedbefore (inner ring) and after (outer ring) surgical resection of tumorfor HCC patient TBR34. Although there is a reduction in both the numberbins showing CNA and the magnitude of CNA in the affected bins after thesurgical resection of the tumor, residual CNA could be observed in thepost-operative plasma sample. The red circle highlights the region inwhich residual CNAs were most obvious. The proportion of bins showing az-score of <−3 or >3 decreased from 57% to 12%.

FIG. 41B shows methylation analysis on bisulfate-treated plasma DNAcollected before (inner ring) and after (outer ring) surgical resectionof tumor for HCC patient TBR34. The magnitude of the hypomethylationdecreased after tumor resection with the mean z-score for thehypomethylated bins having reduced from −7.9 to −4.0. However, theproportion of bins having a z-score <−3 showed an opposite change, withan increase from 41% to 85%. This observation potentially indicates thepresence of residual cancer cells after treatment. Clinically, multiplefoci of tumor nodules were detected in the remaining non-resected liverat 3 months after tumor resection. Lung metastases were observed fromthe 4th month after surgery. The patient died of local recurrence andmetastatic disease 8 months after the operation.

The observations in these two patients (TBR34 and TBR36) suggest thatthe presence of residual cancer-associated changes of CNA andhypomethylation can be used for monitoring and prognosticating cancerpatients after curative-intent treatments. The data also showed that thedegree of change in the amount of plasma CNA detected can be usedsynergistically with assessing the degree of change in the extent ofplasma DNA hypomethylation for prognostication and monitoring oftreatment efficacy.

Accordingly, in some embodiments, one biological sample is obtainedprior to treatment and a second biological sample is obtained aftertreatment (e.g., surgery). First values are obtained for the firstsample, such as the z-scores of regions (e.g., region methylation levelsand normalized values for CNA) and the number of regions showinghypomethylation and CNA (e.g., amplification or deletion). Second valuescan be obtained for the second sample. In another embodiment, a third,or even additional samples, can be obtained after treatment. The numberof regions showing hypomethylation and CNA (e.g., amplification ordeletion) can be obtained from the third or even additional samples.

As described above for FIGS. 40A and 41A, the first number of regionsshowing hypomethylation for the first sample can be compared to thesecond amount of regions showing hypomethylation for the second sample.As described above for FIGS. 40B and 41B, the first amount of regionsshowing hypomethylation for the first sample can be compared to thesecond amount of regions showing hypomethylation for the second sample.Comparing the first amount to the second amount and the first number tothe second number can be used to determine a prognosis of the treatment.In various embodiments, just one of the comparisons can be determinativeof the prognosis or both comparisons can be used. In embodiments inwhich the third or even additional samples are obtained, one or more ofthese samples can be used to determine a prognosis of the treatment,either on their own, or in conjunction with the second sample.

In one implementation, the prognosis is predicted to be worse when afirst difference between the first amount and the second amount is belowa first difference threshold. In another implementation, the prognosisis predicted to be worse when a second difference between the firstnumber and the second number is below a second difference threshold. Thethreshold could be the same or different. In one embodiment, the firstdifference threshold and the second difference threshold are zero. Thus,for the example above, the difference between the values for methylationwould indicate a worse prognosis for patient TBR34.

A prognosis can be better if the first difference and/or the seconddifference are above a same threshold or respective thresholds. Theclassification for the prognosis can depend on how far above or belowthe threshold the differences are. Multiple thresholds could be used toprovide various classifications. Larger differences can predict betteroutcomes and smaller differences (and even negative values) can predictworse outcomes.

In some embodiments, the time points at which the various samples aretaken are also noted. With such temporal parameters, one could determinethe kinetics or the rate of change of the amount. In one embodiment, afast reduction in tumor-associated hypomethylation in plasma and/or afast reduction in the tumor-associated CNA in plasma will be predictiveof good prognosis. Conversely, a static or a fast increase intumor-associated hypomethylation in plasma and/or a static or fastincrease in tumor-associated CNA will be predictive of bad prognosis.The methylation and CNA measurements can be used in conjunction withother clinical parameters (e.g. imaging results or serum biochemistry orprotein markers) for prediction of clinical outcome.

Embodiments can use other samples besides plasma. For example,tumor-associated methylation aberrations (e.g hypomethylation) and/ortumor-associated CNAs can be measured from tumor cells circulating inthe blood of cancer patients, from cell-free DNA or tumor cells in theurine, stools, saliva, sputum, biliary fluid, pancreatic fluid, cervicalswabs, secretions from the reproductive tract (e.g. from the vaginal),ascitic fluid, pleural fluid, semen, sweat and tears.

In various embodiments, tumor-associated methylation aberrations (e.g.hypomethylation) and/or tumor-associated CNAs can be detected from theblood or plasma of patients with breast cancer, lung cancer, colorectalcancer, pancreatic cancer, ovarian cancer, nasopharyngeal carcinoma,cervical cancer, melanoma, brain tumors, etc. Indeed, as methylation andgenetic alterations such as CNAs are universal phenomena in cancer, theapproaches described can be used for all cancer types. The methylationand CNA measurements can be used in conjunction with other clinicalparameters (e.g. imaging results) for prediction of clinical outcome.Embodiments can also be used for the screening and monitoring ofpatients with pre-neoplastic lesions, e.g. adenomas.

Accordingly, in one embodiment, the biological sample is taken prior totreatment, and the CNA and methylation measurements are repeated aftertreatment. The measurements can yield a subsequent first amount ofregions that are determined to exhibit a deletion or an amplificationand can yield a subsequent second amount of regions that are determinedto have a region methylation level exceeding the respective regioncutoff value. The first amount can be compared to the subsequent firstamount, and the second amount can be compared to the subsequent secondamount to determine a prognosis of the organism.

The comparison to determine the prognosis of the organism can includedetermining a first difference between the first amount and thesubsequent first amount, and the first difference can be compared to oneor more first difference thresholds to determine a prognosis. Thecomparison to determine the prognosis of the organism can also includedetermining a second difference between the second amount and thesubsequent second amount, and the second difference can be compared toone or more second difference thresholds. The thresholds may be zero oranother number.

The prognosis can be predicted to be worse when the first difference isbelow a first difference threshold than when the first difference isabove the first difference threshold. The prognosis can be predicted tobe worse when the second difference is below a second differencethreshold than when the second difference is above the second differencethreshold. Examples of treatments include immunotherapy, surgery,radiotherapy, chemotherapy, antibody-based therapy, gene therapy,epigenetic therapy or targeted therapy.

E. Performance

The diagnostic performance for different numbers of sequence reads andof bin size is now described for CNA and methylation analysis.

1. Number of Sequence Reads

According to one embodiment, we analyzed the plasma DNA of 32 healthycontrol subjects, 26 patients suffering from hepatocellular carcinomaand 20 patients suffering from other types of cancers, includingnasopharyngeal carcinoma, breast cancer, lung cancer, neuroendocrinecancer and smooth muscle sarcoma. Twenty-two of the 32 healthy subjectswere randomly selected as the reference group. The mean and standarddeviation (SD) of these 22 reference individuals were used fordetermining the normal range of methylation density and genomicrepresentation. DNA extracted from the plasma sample of each individualwas used for sequencing library construction using the IlluminaPaired-end sequencing kit. The sequencing libraries were then subjectedto bisulfite treatment which converted unmethylated cytosine residues touracil. The bisulfite converted sequencing library for each plasmasample was sequenced using one lane of an Illumina HiSeq2000 sequencer.

After base calling, adapter sequences and low quality bases (i.e.quality score <5) on the fragment ends were removed. The trimmed readsin FASTQ format were then processed by a methylation data analysispipeline called Methy-Pipe (P Jiang et al. 2010, IEEE InternationalConference on Bioinformatics and Biomedicine,doi:10.1109/BIBMW.2010.5703866). In order to align the bisulfiteconverted sequencing reads, we first performed in silico conversion ofall cytosine residues to thymines on the Watson and Crick strandsseparately using the reference human genome (NCBI build 36/hg19). Then,we performed in silico conversion of each cytosine to thymine in all theprocessed reads and kept the positional information of each convertedresidue. SOAP2 was used to align the converted reads to the twopre-converted reference human genomes (R Li et al. 2009 Bioinformatics25:1966-1967), with a maximum of two mismatches allowed for each alignedread. Only reads mappable to a unique genomic location were used fordownstream analysis. Ambiguous reads mapped to both the Watson and Crickstrands and duplicated (clonal) reads were removed. Cytosine residues inthe CpG dinucleotide context were used for downstream methylationanalysis. After alignment, the cytosines originally present on thesequenced reads were recovered based on the positional information keptduring the in silico conversion. The recovered cytosines among the CpGdinucleotides were scored as methylated. Thymines among the CpGdinucleotides were scored as unmethylated.

For methylation analysis, the genome was divided into equal-sized bins.The size of bins tested include 50 kb, 100 kb, 200 kb and 1 Mb. Themethylation density for each bin was calculated as the number ofmethylated cytosines in the context of CpG dinucleotide divided by thetotal number of cytosines at CpG positions. In other embodiments, thebin size can be non-equal across the genome. In one embodiment, each binamongst such bins of non-equal sizes is compared across multiplesubjects.

To determine if the plasma methylation density of a tested case wasnormal, the methylation density was compared to the results of thereference group. Twenty-two of the 32 healthy subjects were randomlyselected as the reference group for the calculation of the methylationz-score (Z_(meth)).

$Z_{meth} = \frac{{MD_{test}} - {\overset{\_}{MD}}_{ref}}{MD_{SD}}$

where MD_(test) was the methylation density of the tested case for aparticular 1-Mb bin; MD _(ref) was the mean methylation density of thereference group for the corresponding bin; and MD_(SD) was the SD of themethylation density of the reference group for the corresponding bin.

For CNA analysis, the number of sequenced reads mapping to each 1-Mb binwas determined (K C A Chan el al. 2013 Clin Chem 59:211-24). Sequencedread density was determined for each bin after correction for GC biasusing Locally Weighted Scatter Plot Smoothing regression as previouslydescribed (E Z Chen et al. 2011 PLoS One 6: e21791). For plasmaanalysis, the sequenced read density of the tested case was comparedwith the reference group to calculate the CNA z-score (Z_(CNA)):

$Z_{CNA} = \frac{{RD}_{test} - {\overset{\_}{RD}}_{ref}}{{RD}_{SD}}$

where RD_(test) was the sequenced read density of the tested case for aparticular 1-Mb bin; RD _(ref) was the mean sequenced read density ofthe reference group for the corresponding bin; and RD_(SD) was the SD ofthe sequenced read density of the reference group for the correspondingbin. A bin was defined to exhibit CNA if the Z_(CNA) of the bin was <−3or >3.

A mean of 93 million aligned reads (range: 39 million to 142 million)were obtained per case. To evaluate the effect of reduction of thenumber of sequenced reads on the diagnostic performance, we randomlyselected 10 million aligned reads from each case. The same set ofreference individuals was used for establishing the reference range ofeach 1-Mb bin for the dataset with reduced sequenced reads. Thepercentage of bins showing significant hypomethylation, i.e. Z_(meth)<−3and the percentage of bins with CNA, i.e. Z_(CNA)<−3 or >3, weredetermined for each case. Receiver operating characteristics (ROC)curves were used to illustrate the diagnostic performance of genomewidehypomethylation and CNA analyses for the datasets with all sequencedreads from 1 lane and 10 million reads per case. In the ROC analysis,all the 32 healthy subjects were used for the analysis.

FIG. 42 shows a diagram of diagnostic performance of genomewidehypomethylation analysis with different number of sequenced reads. Forhypomethylation analysis, the areas-under-curve for the ROC curves werenot significantly different between the two datasets which analyzed allsequenced reads from one lane and 10 million reads per case (P=0.761).For CNA analysis, the diagnostic performance deteriorated with asignificant reduction in the areas-under-curve when the number ofsequenced reads reduced from using the data of one lane to 10 million(P<0.001).

2. Effect of Using Different Bin Size

In addition to dividing the genome into 1-Mb bins, we also explored ifsmaller bin sizes can be used. Theoretically, the use of smaller binscan potentially reduce the variability in methylation density within abin. This is because the methylation density between different genomicregions can vary widely. When a bin is bigger, the chance of includingregions with different methylation densities would increase and, hence,would lead to an overall increase in the variability in methylationdensity of the bins.

Although the use of smaller bin size may potentially reduce thevariability in methylation density related to inter-regional difference,this would on the other hand reduce the number of sequenced reads mappedto a particular bin. The reduction in reads mapping to individual binswould increase the variability due to sampling variation. The optimalbin size that can give rise to lowest overall variability in methylationdensity can be experimentally determined for the requirements of aparticular diagnostic application, e.g. the total number of sequencedreads per sample and the type of DNA sequencer used.

FIG. 43 is a diagram showing ROC curves for the detection of cancerbased on genomewide hypomethylation analysis with different bin sizes(50 kb, 100 kb, 200 kb and 1 Mb). The P-values shown are forarea-under-curve comparison with a bin size of 1 Mb. A trend ofimprovement can be seen when the bin size was reduced from 1 Mb to 200kb.

F. Cumulative Probability Score

The amount of regions for methylation and CNA can be various values.Examples above described a number of regions exceeding a cutoff value ora percentage of such regions that showed significant hypomethylation orCNA as a parameter for classifying if a sample was associated withcancer. Such approaches do not take into account the magnitude of theaberration for individual bins. For example, a bin with a Z_(meth) of−3.5 would be the same as a bin with a Z_(meth) of −30 as both of themwould be classified as having significant hypomethylation. However, thedegree of hypomethylation changes in the plasma, i.e. the magnitude ofthe Z_(meth) value, is affected by the amount of cancer-associated DNAin the sample and, hence, may supplement the information of percentageof bins showing aberrations to reflect tumor load. A higher fractionalconcentration of tumoral DNA in the plasma sample would lead to a lowermethylation density and this would translate to a lower Z_(meth) value.

1. Cumulative Probability Score as a Diagnostic Parameter

To make use of the information from the magnitude of the aberrations, wedevelop an approach called cumulative probability (CP) score. Base onnormal distribution probability function, each Z_(meth) value wastranslated to a probability of having such an observation by chance.

The CP score was calculated as:

CP score=Σ−log(Prob_(i)) for bin(i) with Z _(meth)<−3

where Prob_(i) is the probability for the Z_(meth) of bin(i) accordingto the Student's t distribution with 3 degree of freedom, and log is thenatural logarithm function. In another embodiment, a logarithm with base10 (or other number) can be used. In other embodiments, otherdistributions, for example, but not limited to normal distribution andgamma distribution, can be applied to transform the z-score to CP.

A larger CP score indicates a lower probability of having such adeviated methylation density in a normal population by chance.Therefore, a high CP score would indicate a higher chance of havingabnormally hypomethylated DNA in the sample, e.g. the presence ofcancer-associated DNA.

Compared with the percentage of bins showing aberration, the CP scoremeasurement has a higher dynamic range. While the tumor loads betweendifferent patients can vary widely, the larger range of CP values wouldbe useful for reflecting the tumor loads of patients with relativelyhigh and relatively low tumor loads. In addition, the use of CP scorescan potentially be more sensitive for detecting the changes in theconcentration of tumor-associated DNA in plasma. This is advantageousfor the monitoring of treatment response and prognostication. Hence, areduction in CP scores during treatment is indicative of a good responseto treatment. A lack of reduction or even increase in CP scores duringtreatment would indicate poor or lack of response. For prognostication,a high CP score is indicative of high tumor load and is suggestive ofbad prognosis (e.g. higher chance of death or tumor progression).

FIG. 44A shows a diagnostic performance for cumulative probability (CP)and percentage of bins with aberrations. There was no significantdifference between the areas-under-curve for the two types of diagnosticalgorithm (P=0.791).

FIG. 44B shows diagnostic performances for the plasma analysis forglobal hypomethylation, CpG island hypermethylation and CNA. With onelane of sequencing per sample (200 kb bin size for hypomethylationanalysis and 1 Mb bin size for CNA, and CpG islands defined according tothe database hosted by The University of California, Santa Cruz (UCSC)),the areas-under-curve for all the three types of analyses were above0.90.

In the subsequent analyses, the highest CP score in the control subjectswas used as the cutoff for each of the three types of analyses. Theselection of these cutoffs gave a diagnostic specificity of 100%. Thediagnostic sensitivities for general hypomethylation, CpG islandhypermethylation and CNA analyses were 78%, 89% and 52%, respectively.In 43 out of the 46 cancer patients, at least one of the three types ofaberrations was detected, thus, giving rise to a sensitivity of 93.4%and a specificity of 100%. Our results indicate that the three types ofanalyses can be used synergistically for the detection of cancer.

FIG. 45 shows a table with results for global hypomethylation, CpGisland hypermethylation and CNA in hepatocellular carcinoma patients.The CP score cutoff values for the three types of analysis were 960, 2.9and 211, respectively. Positive CP score results were in bold andunderlined.

FIG. 46 shows a table with results for global hypomethylation, CpGisland hypermethylation and CNA in patients suffering from cancers otherthan hepatocellular carcinoma. The CP score cutoff values for the threetypes of analysis were 960, 2.9 and 211, respectively. Positive CP scoreresults were in bold and underlined.

2. Application of CP Score for Cancer Monitoring

Serial samples were collected from a HCC patient TBR34 before and aftertreatment. The samples were analyzed for global hypomethylation.

FIG. 47 shows a serial analysis for plasma methylation for case TBR34.The innermost ring shows the methylation density of the buffy coat(black) and tumor tissues (purple). For the plasma samples, the Z_(meth)is shown for each 1 Mb bin. The difference between two lines representsa Z_(meth) difference of 5. Red and grey dots represent bins withhypomethylation and no change in methylation density compared with thereference group. From the 2^(nd) inner ring outwards are the plasmasamples taken before treatment, at 3 days and 2 months after tumorresection, respectively. Before treatment, a high degree ofhypomethylation could be observed in the plasma and over 18.5% of thebins had a Z_(meth) of <−10. At 3 days after tumor resection, it couldbe observed that the degree of hypomethylation was reduced in the plasmawith none of the bins with Z_(meth) of <−10.

TABLE 5 Methylation analysis Percentage of bins Cumulative showingsignificant probability Summative Case no. Time point hypomethylation(CP) score z-score TBR34 Before OT 62.6% 37,573 14,285 3 days after OT80.5% 17,777  9,195 2 months after 40.1% 15,087  5,201

Table 5 shows that although the magnitude of the hypomethylation changesreduced at 3 days after surgical resection of the tumor, the percentageof bins exhibiting aberration showed a paradoxical increase. On theother hand, the CP score more accurately revealed the reduction in thedegree of hypomethylation in plasma and may be more reflective of thechanges in tumor load.

At 2 months after OT, there was still a significant percentage of binsshowing hypomethylation changes. The CP score also remained static atapproximately 15,000. This patient was later diagnosed as havingmulti-focal tumor deposits (previously unknown at the time of surgery)in the remaining non-resected liver at 3 months and was noted to havemultiple lung metastases at 4 months after the operation. The patientdied of metastatic disease at 8 months after the operation. Theseresults suggested that the CP score might be more powerful thanpercentage of bins with aberration for reflecting tumor load.

Overall, the CP can be useful for applications that require measuringthe amount of tumor DNA in plasma. Examples of such applicationsinclude: prognostication and monitoring of cancer patients (e.g. toobserve response to treatment, or to observe tumor progression).

The summative z-score is a direct sum of the z-scores, i.e., withoutconverting to a probability. In this example, the summative z-scoreshows the same behavior as the CP score. In other instances, CP can bemore sensitive than the summative z-score for monitoring residualdisease because of the larger dynamic range for the CP score.

X. CNA Impact on Methylation

The use of CNA and methylation to determine respective classificationsfor a level of cancer, where the classifications are combined to providea third classification, was described above. Besides such a combination,CNA can be used to change cutoff values for the methylation analysis andto identify false-positives by comparing methylation levels for groupsof regions having different CNA characteristics. For instance, themethylation level for over-abundance (e.g., Z_(CNA)>3) can be comparedto methylation level for normal abundance (e.g., −3<Z_(CNA)<3). First,the impact of CNA on methylation levels is described.

A. Alteration in Methylation Density at Regions with Chromosomal Gainsand Losses

As tumor tissues generally show an overall hypomethylation, the presenceof tumor-derived DNA in the plasma of cancer patients would lead to thereduction in the methylation density when compared with non-cancersubjects. The degree of hypomethylation in the plasma of cancer patientsis theoretically proportional to the fractional concentration oftumor-derived DNA in the plasma sample.

For regions showing a chromosomal gain in the tumor tissues, anadditional dosage of tumor DNA would be released from the amplified DNAsegments into the plasma. This increased contribution of tumoral DNA tothe plasma would theoretically lead to a higher degree ofhypomethylation in the plasma DNA for the affected region. An additionalfactor is that genomic regions showing amplification would be expectedto confer growth advantage to the tumor cells, and thus would beexpected to be expressed. Such regions are generally hypomethylated.

In contrast, for regions that show chromosomal loss in the tumor tissue,the reduced contribution of tumoral DNA to plasma would lead to a lowerdegree of hypomethylation compared with regions with no copy numberchange. An additional factor is that genomic regions that are deleted intumor cells might contain tumor suppressor genes and it might beadvantageous to tumor cells to have such regions silenced. Thus, suchregions are expected to have a higher chance of being hypermethylated.

Here, we use the results of two HCC patients (TBR34 and TBR36) toillustrate this effect. FIGS. 48A (TBR36) and 49A (TBR34) have circleshighlighting regions with chromosomal gains or losses and thecorresponding methylation analysis. FIGS. 48B and 49B show plots ofmethylation z-scores for losses, normal, and gains for patients TBR36and TBR34, respectively.

FIG. 48A shows Circos plots demonstrating the CNA (inner ring) andmethylation changes (outer ring) in the bisulfite-treated plasma DNA forHCC patient TBR36. The red circles highlight the regions withchromosomal gains or losses. Regions showing chromosomal gains were morehypomethylated than regions without copy number changes. Regions showingchromosomal losses were less hypomethylated than regions without copynumber changes. FIG. 48B is a plot of methylation z-scores for regionswith chromosomal gains and loss, and regions without copy number changefor the HCC patient TBR36. Compared with regions without copy changes,regions with chromosomal gains had more negative z-scores (morehypomethylation) and regions with chromosomal losses had less negativez-scores (less hypomethylated).

FIG. 49A shows Circos plots demonstrating the CNA (inner ring) andmethylation changes (outer ring) in the bisulfite-treated plasma DNA forHCC patient TBR34. FIG. 49B is a plot of methylation z-scores forregions with chromosomal gains and loss, and regions without copy numberchange for the HCC patient TBR34. The difference in methylationdensities between regions with chromosomal gains and losses was largerin patient TBR36 than in patient TBR34 because the fractionalconcentration of tumor-derived DNA in the former patient was higher.

In this example, the regions used to determine CNA are the same as theregions used to determine methylation. In one embodiment, the respectiveregion cutoff values are dependent on whether the respective regionexhibits a deletion or an amplification. In one implementation, arespective region cutoff value (e.g., the z-score cutoff used todetermine hypomethylation) has a larger magnitude when the respectiveregion exhibits an amplification than when no amplification is exhibited(e.g., the magnitude could be greater than 3, and a cutoff of less than−3 can be used). Thus, for testing hypomethylation, a respective regioncutoff value can have a larger negative value when the respective regionexhibits an amplification than when no amplification is exhibited. Suchan implementation is expected to improve the specificity of the test fordetecting cancer.

In another implementation, a respective region cutoff value has asmaller magnitude (e.g., less than 3) than when the respective regionexhibits a deletion than when no deletion is exhibited. Thus, fortesting hypomethylation, a respective region cutoff value can have aless negative value when the respective region exhibits a deletion thanwhen no deletion is exhibited. Such an implementation is expected toimprove the sensitivity of the test for detecting cancer. The adjustmentof the cutoff values in the above implementations can be changeddepending on the desired sensitivity and specificity for a particularlydiagnostic scenario. In other embodiments, the methylation and CNAmeasurements can be used in conjunction with other clinical parameters(e.g. imaging results or serum biochemistry) for prediction of cancer.

B. Using CNA to Select Regions

As described above, we have shown that the plasma methylation densitywould be altered in regions having copy number aberrations in the tumortissues. At regions with copy number gain in the tumor tissue, increasedcontribution of hypomethylated tumoral DNA to the plasma would lead to alarger degree of hypomethylation of plasma DNA compared with regionswithout a copy number aberration. Conversely, at regions with copynumber loss in the tumor tissue, the reduced contribution ofhypomethylated cancer-derived DNA to the plasma would lead to a lesserdegree of hypomethylation of plasma DNA. This relationship between themethylation density of plasma DNA and the relative representation canpotentially be used for differentiating hypomethylation resultsassociated with the presence of cancer-associated DNA and othernon-cancerous causes (e.g., SLE) of hypomethylation in plasma DNA.

To illustrate this approach, we analyzed the plasma samples of twohepatocellular carcinoma (HCC) patients and two patients with SLEwithout a cancer. These two SLE patients (SLE04 and SLE10) showed theapparent presence of hypomethylation and CNAs in plasma. For patientSLE04, 84% bins showed hypomethylation and 11.2% bins showed CNA. Forpatient SLE10, 10.3% bins showed hypomethylation and 5.7% bins showedCNA.

FIGS. 50A and 50B show results of plasma hypomethylation and CNAanalysis for SLE patients SLE04 and SLE10. The outer circle shows themethylation z-scores (Z_(meth)) at 1 Mb resolution. The bins withmethylation Z_(meth)<−3 were in red and those with Z_(meth)>−3 were ingrey. The inner circle shows the CNA z-scores (Z_(CNA)). The green, redand grey dots represent bins with Z_(CNA)>3, <3 and between −3 to 3,respectively. In these two SLE patients, hypomethylation and CNA changeswere observed in plasma.

To determine if the changes in methylation and CNA were consistent withthe presence of cancer-derived DNA in plasma, we compared the Z_(meth)for regions with Z_(CNA)>3, <−3 and between −3 to 3. For methylationchanges and CNA contributed by cancer-derived DNA in plasma, regionswith Z_(CNA)<−3 would be expected to be less hypomethylated and had lessnegative Z_(meth). In contrast, regions with Z_(CNA)>3 would be expectedto be more hypomethylated and had more negative Z_(meth). Forillustration purpose, we applied one-sided rank sum test to compare theZ_(meth) for regions with CNA (i.e. regions with Z_(CNA)<−3 or >3) withregions without CNA (i.e. regions with Z_(CNA) between −3 and 3). Inother embodiments, other statistical tests, for example but not limitedto Student's t-test, analysis of variance (ANOVA) test andKruskal-Wallis test can be used.

FIGS. 51A and 51B show Z_(meth) analysis for regions with and withoutCNA for the plasma of two HCC patients (TBR34 and TBR36). Regions withZ_(CNA)<−3 and >3 represent regions with under- and over-representationin plasma, respectively. In both TBR34 and TBR36, regions that wereunder-represented in plasma (i.e. regions with Z_(CNA)<−3) hadsignificantly higher Z_(meth) (P-value <10⁻⁵, one-sided rank sum test)than regions with normal representation in plasma (i.e. regions withZ_(CNA) between −3 and 3). A normal representation correspond to thatexpected for a euploid genome. For regions with over-representation inplasma (i.e. regions with Z_(CNA)>3), they had significantly lowerZ_(meth) than regions with normal representation in plasma (P-value<10⁻⁵, one-sided rank sum test). All these changes were consistent withthe presence of hypomethylated tumoral DNA in the plasma samples.

FIGS. 51C and 51D show Z_(meth) analysis for regions with and withoutCNA for the plasma of two SLE patients (SLE04 and SLE10). Regions withZ_(CNA)<−3 and >3 represent regions with under- and over-representationin plasma, respectively. For SLE04, regions that were under-representedin plasma (i.e. regions with Z_(CNA)<−3) did not have significantlyhigher Z_(meth) (P-value=0.99, one-sided rank sum test) than regionswith normal representation in plasma (i.e. regions with Z_(CNA) between−3 and 3) and regions with over-representation in plasma (i.e. regionswith Z_(CNA)>3) did not have significantly lower Z_(meth) than regionswith normal representation in plasma (P-value=0.68, one-sided rank sumtest). These results were different from the expected changes due to thepresence of tumor-derived hypomethylated DNA in plasma. Similarly, forSLE10, regions with Z_(CNA)<−3 did not have significantly higherZ_(meth) than regions with Z_(CNA) between −3 and 3 (P-value=0.99,one-sided rank sum test).

A reason of not having the typical cancer-associated pattern betweenZ_(meth) and Z_(CNA) in the SLE patients is that, in the SLE patients,the CNA is not present in a specific cell type that also exhibitshypomethylation. Instead, the observed apparent presence of CNA andhypomethylation is due to the altered size distribution of circulatingDNA in SLE patients. The altered size distribution could potentiallyalter the sequenced read densities for different genomic regions leadingto apparent CNAs as the references were derived from healthy subjects.As described in the previous sections, there is a correlation betweenthe size of a circulating DNA fragment and its methylation density.Therefore, the altered size distribution can also lead to an aberrantmethylation.

Although the regions with Z_(CNA)>3 had slightly lower methylationlevels than regions with Z_(CNA) between −3 and 3, the p-value for thecomparison was far higher than those observed in two cancer patients. Inone embodiment, the p-value can be used as a parameter to determine thelikelihood of a tested case for having a cancer. In another embodiment,the difference in Z_(meth) between regions with normal and aberrantrepresentation can be used as a parameter for indicating the likelihoodof the presence of cancer. In one embodiment, a group of cancer patientscan be used to establish the correlation between Z_(meth) and Z_(CNA)and to determine the thresholds for different parameters so as toindicate the changes are consistent with the presence of cancer-derivedhypomethylated DNA in the tested plasma sample.

Accordingly, in one embodiment, a CNA analysis can be performed todetermine a first set of regions that all exhibit one of: a deletion, anamplification, or normal representation. For example, the first set ofregions can all exhibit a deletion, or all exhibit an amplification, orall exhibit a normal representation (e.g., have a normal first amount ofregions, such as a normal Z_(meth)). A methylation level can bedetermined for this first set of regions (e.g., the first methylationlevel of method 2800 can correspond to the first set of regions).

The CNA analysis can determine a second set of regions that all exhibita second of: a deletion, an amplification, or normal representation. Thesecond set of regions would exhibit differently than the first set. Forexample, if the first set of regions were normal, then the second set ofregions can exhibit a deletion or an amplification. A second methylationlevel can be calculated based on the respective numbers of DNA moleculesmethylated at sites in the second set of regions.

A parameter can then be computed between the first methylation level andthe second methylation. For example, a difference or ratio can becomputed and compared to a cutoff value. The difference or ratio canalso be subjected to a probability distribution (e.g., as part of astatistical test) to determine the probability of obtaining the value,and this probability can be compared to a cutoff value to determine alevel of cancer based on methylation levels. Such a cutoff can be chosento differentiate samples having cancer and those not having cancer(e.g., SLE).

In one embodiment, a methylation level can be determined for the firstset of region or a mix of regions (i.e., mix of regions showingamplification, deletion, and normal). This methylation level can then becompared to a first cutoff as part of a first stage of analysis. If thecutoff is exceeded, thereby indicating a possibility of cancer, then theanalysis above can be performed to determine whether the indication wasa false positive. The final classification for the level of cancer canthus include the comparison of the parameter for the two methylationlevels to a second cutoff.

The first methylation level can be a statistical value (e.g., average ormedian) of region methylation levels calculated for each region of thefirst set of regions. The second methylation level can also be astatistical value of region methylation levels calculated for eachregion of the second set of regions. As examples. the statistical valuescan be determined using one-sided rank sum test, Student's t-test,analysis of variance (ANOVA) test, or Kruskal-Wallis test.

XI. Cancer Type Classification

In addition to determining whether an organism has cancer or not,embodiments can identify a type of cancer associated with the sample.This identification of cancer type can use patterns of globalhypomethylation, CpG island hypermethylation, and/or CNA. The patternscan involve clustering of patients with a known diagnosis using measuredregion methylation levels, respective CNA values for regions, andmethylation level for CpG islands. The results below show that organismswith a similar type of cancer have similar values for the regions andCpG islands, as well as the non-cancer patients having similar values.In the clustering, each of the values for a region or island can be aseparate dimension in the clustering process.

It has been known that the same type of cancers would share similargenetic and epigenetic changes (E Gebhart et al. 2004 Cytogenet GenomeRes; 104: 352-358; P A Jones et al. 2007 Cell; 128: 683-692). Below, wedescribe how the patterns of CNA and methylation changes detected in theplasma are useful for inferring the origin or type of the cancer. Theplasma DNA samples from the HCC patients, non-HCC patients and healthycontrol subjects were classified using, for example, hierarchicalclustering analysis. The analysis was performed using, for example, theheatmap.2 function in R script package(cran.r-project.org/web/packages/gplots/gplots.pdf).

To illustrate the potential of this approach, we used two sets ofcriteria (group A and group B) as examples to identify useful featuresfor the classification of the plasma samples (See Table 6). In otherembodiments, other criteria can be used for identifying the features.The features used included global CNA at 1 Mb resolution, globalmethylation density at 1 Mb resolution and CpG island methylation.

TABLE 6 Global methylation at 1 Mb resolution Group A criteria Group Bcriteria Criteria >20 cancer cases >20 cancer cases with a z-score > 3with a z-score > 2.5 or < −3 or < −2.5 No. of features 584 1,911identified CNA features Group A criteria Group B criteria Criteria >10cancer cases >10 cancer cases with a z-score > 3 with a or < −3z-score > 2.5 < −2.5 No. of features 355 759 identified CpG islandmethylation Group A criteria Group B criteria Criteria >5 cancercases >1 cancer cases with a methylation with a methylation densitydiffering density differing from the mean of from the mean of thereference by the reference by 2% at the 2% at the particular CpGparticular CpG islands islands No. of features 110 191 identified

In the first two examples, we used all the CNA, global methylation at 1Mb resolution and CpG island methylation features for theclassification. In other embodiments, other criteria, for example, butnot limited to the precision of measuring the feature in the plasma ofreference group, can be used.

FIG. 52A shows hierarchical clustering analysis for plasma samples fromHCC patients, non-HCC cancer patients and healthy control subjects usingall the 1,130 group A features including 355 CNAs, 584 globalmethylation features at 1 Mb resolution and the methylation status of110 CpG islands. The upper side color bar represents the sample groups:green, blue and red represent the healthy subjects, HCC and non-HCCcancer patients, respectively. In general, the three groups of subjectstended to cluster together. The vertical axis represents the classifyingfeatures. Features with similar patterns across different subjects wereclustered together. These results suggest that the patterns of CpGisland methylation changes, genomewide methylation changes at 1 Mbresolution and CNAs in plasma can potentially be used for determiningthe origin of the cancer in patients with unknown primaries.

FIG. 52B shows hierarchical clustering analysis for plasma samples fromHCC patients, non-HCC cancer patients and healthy control subjects usingall the 2,780 group B features including 759 CNA, 1,911 globalmethylation at 1 Mb resolution and the methylation status of 191 CpGislands. The upper side color bar represents the sample groups: green,blue and red represent the healthy subjects, HCC and non-HCC cancerpatients, respectively. In general, the three groups of subjects tendedto cluster together. The vertical axis represents the classifyingfeatures. Features with similar patterns across different subjects wereclustered together. These results suggest that the patterns of differentsets of CpG islands methylation changes, genomewide methylation changesat 1 Mb resolution and CNAs in plasma can be used for determining theorigin of the cancer in patients with unknown primaries. The selectionof the classification features can be adjusted for specificapplications. In addition, weight can be given to the cancer typeprediction according to the prior probabilities of the subjects fordifferent types of cancers. For example, patients with chronic viralhepatitis are prone to the development of hepatocellular carcinoma andchronic smokers are prone to development of lung cancer. Thus, aweighted probability of the type of cancer can be calculated using, forexample but not limited to, logistic, multiple, or clusteringregression.

In other embodiments, a single type of features can be used for theclassification analysis. For example, in the following examples, onlythe global methylation at 1 Mb resolution, the CpG islandhypermethylation or the CNAs at 1 Mb resolution were used for thehierarchical clustering analysis. The differentiation power may bedifferent when different features are used. Further refinement of theclassification features can potentially improve the classificationaccuracies.

FIG. 53A shows hierarchical clustering analysis for plasma samples fromHCC patients, non-HCC cancer patients and healthy control subjects usingthe group A CpG island methylation features. Generally, the cancerpatients clustered together and the non-cancer subjects were in anothercluster. However, the HCC and non-HCC patients were less separatedcompared with using all three types of features.

FIG. 53B shows hierarchical clustering analysis for plasma samples fromHCC patients, non-HCC cancer patients and healthy control subjects usingthe group A global methylation densities at 1 Mb resolution asclassifying features. Preferential clustering of HCC and non-HCCpatients was observed.

FIG. 54A shows a hierarchical clustering analysis for plasma samplesfrom HCC patients, non-HCC cancer patients and healthy control subjectsusing the group A global CNAs at 1 Mb resolution as classifyingfeatures. Preferential clustering of HCC and non-HCC patients was seen.

FIG. 54B shows a hierarchical clustering analysis for plasma samplesfrom HCC patients, non-HCC cancer patients and healthy control subjectsusing the group B CpG islands methylation densities as classifyingfeatures. Preferential clustering of HCC and non-HCC cancer patientscould be observed.

FIG. 55A shows a hierarchical clustering analysis for plasma samplesfrom HCC patients, non-HCC cancer patients and healthy control subjectsusing the group B global methylation densities at 1 Mb resolution asclassifying features. Preferential clustering of HCC and non-HCC cancerpatients could be observed.

FIG. 55B shows a hierarchical clustering analysis for plasma samplesfrom HCC patients, non-HCC cancer patients and healthy control subjectsusing the group B global CNAs at 1 Mb resolution as classifyingfeatures. Preferential clustering of HCC and non-HCC cancer patientscould be observed.

These hierarchical clustering results for plasma samples suggest thatthe combination of different features can potentially be used for theidentification of the primary cancer types. Further refinement of theselection criteria can potentially further improve the accuracy of theclassification.

Accordingly, in one embodiment, when a methylation classificationindicates that cancer exists for the organism, a type of cancerassociated with the organism can be identified by comparing amethylation level (e.g., first methylation from method 2800 or anyregion methylation level) to a corresponding value determined from otherorganisms (i.e., other organisms of the same type, such as humans). Thecorresponding value could be for a same region or set of sites that themethylation level was calculated. At least two of the other organismsare identified as having different types of cancer. For example, thecorresponding values can be organized into clusters, where two clustersare associated with different cancers.

Further, when CNA and methylation are used together to obtain a thirdclassification of the level of cancer, CNA and methylation features canbe compared to corresponding values from other organisms. For example,the first amount of regions (e.g., from FIG. 36) exhibiting a deletionor amplification can be compared to corresponding values determined fromthe other organisms to identify the type of cancer associated with theorganism.

In some embodiments, the methylation features are the region methylationlevels of a plurality of regions of the genome. Regions that aredetermined to have a region methylation level exceeding the respectiveregion cutoff value can be used, e.g., region methylation levels of theorganism can be compared to region methylation levels of other organismsfor the same regions of the genome. The comparison can allow one todifferentiate cancer types, or just provide an additional filter toconfirm cancer (e.g., to identify false positives). Thus, it can bedetermined whether the organism has the first type of cancer, absence ofcancer, or the second type of cancer based on the comparison.

The other organisms (along with the one being tested) can be clusteredusing the region methylation levels. Thus, a comparison of the regionmethylation levels can be used to determine which cluster the organismbelongs. The clustering can also use CNA normalized values for regionsthat are determined to exhibit a deletion or an amplification, as isdescribed above. And, the clustering can use the respective methylationdensities of hypermethylated CpG islands.

To illustrate the principle of this method, we show an example of usinglogistic regression for the classification of two unknown samples. Thepurpose of this classification was to determine if these two sampleswere HCCs or non-HCC cancers. A training set of samples were compiledwhich included 23 plasma samples collected from HCC patients and 18samples from patients suffering from cancer other than HCC. Thus, therewere a total of 41 cases in the training set. In this example, 13features were selected, including five features on the methylation ofCpG islands (X1-X5), six features on the methylation of 1-Mb regions(X6-X11) and 2 features on the CNA of 1-Mb regions (X12-X13). The CpGmethylation features were selected based on the criterion of at least 15cases in the training set having a z-score of >3 or <−3. The 1-Mbmethylation features were selected based on the criterion of at least 39cases in the training set having a z-score of >3 or <−3. The CNAfeatures were selected based on the criterion of at least 20 caseshaving a z-score >3 or <−3. Logistic regression was performed on thesamples of this training set so as to determine the regressioncoefficient for each of the features (X1-X13). Features with regressioncoefficients of the larger magnitudes (irrespective of whether it is ina positive or negative sense) offer better discrimination between HCCand non-HCC samples. The z-scores of each case for the respectivefeatures were used as the input values of the independent variables.Then two plasma samples, one from a HCC patient (TBR36) and one from apatient suffering from lung cancer (TBR177) were analyzed for the 13features.

In this cancer type classification analysis, these two samples wereassumed to be collected from patients suffering from cancers of unknownorigin. For each sample, the z-scores for the respective feature wereput into the logistic regression equation to determine the naturallogarithm of the odds ratio (ln(odds ratio)) where the odds ratiorepresented the ratio of probabilities of having HCC and not having HCC(HCC/non-HCC).

Table 7 shows the regression coefficients for the 13 features of thelogistic regression equation. The z-scores for the respective featuresof the two tested cases (TBR36 and TBR177) are also shown. The ln(oddsratio) of HCC for TBR36 and TBR177 were 37.03 and −4.37, respectively.From these odds ratios, the probability of the plasma samples beingcollected from HCC patients were calculated as >99.9% and 1%,respectively. In short, TBR36 had a high likelihood of being a samplefrom a HCC patient while TBR177 had a low likelihood of being a samplefrom a HCC patient.

TABLE 7 z-score of the Regression respective feature Feature coefficientTBR36 TBR177 X1 −2.9575 14.8 0 X2 2.2534 21.3 0 X3 −1.5099 6.1 0 X4−0.236 34.0 0 X5 0.7426 17.3 0 X6 −0.6682 −26.3 −1.5 X7 −0.2828 −13.9−2.6 X8 −0.7281 −9.4 −4.4 X9 1.0581 −7.8 −3.7  X10 0.3877 −20.8 −4.3 X11 0.3534 −15.5 −3.1  X12 −1.1826 4.8 3.3  X13 −0.3805 −11.7 −1.41n(odds ratio) 37.03 −4.37463

In other embodiments, hierarchical clustering regression, classificationtree analysis and other regression models can be used for determiningthe likely primary origin of the cancer.

XII. Materials and Methods

A. Preparation of Bisulfite-Treated DNA Libraries and Sequencing

Genomic DNA (5 μg) added with 0.5% (w/w) unmethylated lambda DNA(Promega) was fragmented by a Covaris 5220 System (Covaris) toapproximately 200 bp in length. DNA libraries were prepared using thePaired-End Sequencing Sample Preparation Kit (Illumina) according to themanufacturer's instructions, except that methylated adapters (Illumina)were ligated to the DNA fragments. Following two rounds of purificationusing AMPure XP magnetic beads (Beckman Coulter), the ligation productswere split into 2 portions, one of which was subjected to 2 rounds ofbisulfite modification with an EpiTect Bisulfite Kit (Qiagen).Unmethylated cytosines at CpG sites in the inserts were converted touracils while the methylated cytosines remained unchanged. Theadapter-ligated DNA molecules, either treated or untreated with sodiumbisulfite, were enriched by 10 cycles of PCR using the following recipe:2.5 U PfuTurboCx hotstart DNA polymerase (Agilent Technologies),1×PfuTurboCx reaction buffer, 25 μM dNTPs, 1 μl PCR Primer PE 1.0 and 1μl PCR Primer PE 2.0 (Illumina) in a 50 μl-reaction. The thermocyclingprofile was: 95° C. for 2 min, 98° C. for 30 s, then 10 cycles of 98° C.for 15 s, 60° C. for 30 s and 72° C. for 4 min, with a final step of 72°C. for 10 min (R Lister, et al. 2009 Nature; 462: 315-322). The PCRproducts were purified using AMPure XP magnetic beads.

Plasma DNA extracted from 3.2-4 ml of maternal plasma samples was spikedwith fragmented lambda DNA (25 pg per ml plasma) and subjected tolibrary construction as described above (R W K Chiu et al. 2011 BMJ;342: c7401). After ligating to the methylated adapters, the ligationproducts were split into 2 halves and a portion was subjected to 2rounds of bisulfite modification. The bisulfite-treated or untreatedligation products were then enriched by 10 cycles of PCR as describedabove.

Bisulfite-treated or untreated DNA libraries were sequenced for 75 bp ina paired-end format on HiSeq2000 instruments (Illumina). DNA clusterswere generated with a Paired-End Cluster Generation Kit v3 on a cBotinstrument (Illumina). Real-time image analysis and base calling wereperformed using the HiSeq Control Software (HCS) v1.4 and Real TimeAnalysis (RTA) Software v1.13 (Illumina), by which the automated matrixand phasing calculations were based on the spiked-in PhiX control v3sequenced with the DNA libraries.

B. Sequence Alignment and Identification of Methylated Cytosines

After base calling, adapter sequences and low quality bases (i.e.quality score <20) on the fragment ends were removed. The trimmed readsin FASTQ format were then processed by a methylation data analysispipeline called Methy-Pipe (P Jiang, et al. Methy-Pipe: An integratedbioinformatics data analysis pipeline for whole genome methylomeanalysis, paper presented at the IEEE International Conference onBioinformatics and Biomedicine Workshops, Hong Kong, 18 to 21 Dec.2010). In order to align the bisulfite converted sequencing reads, wefirst performed in silico conversion of all cytosine residues tothymines, on the Watson and Crick strands separately, using thereference human genome (NCBI build 36/hg18). We then performed in silicoconversion of each cytosine to thymine in all the processed reads andkept the positional information of each converted residue. SOAP2 (R Li,et al. 2009 Bioinformatics; 25: 1966-1967) was used to align theconverted reads to the two pre-converted reference human genomes, with amaximum of two mismatches allowed for each aligned read. Only readsmappable to a unique genomic location were selected. Ambiguous readswhich mapped to both the Watson and Crick strands and duplicated(clonal) reads which had the same start and end genomic positions wereremoved. Sequenced reads with insert size ≤600 bp were retained for themethylation and size analyses.

Cytosine residues in the CpG dinucleotide context were the major targetsfor the downstream DNA methylation studies. After alignment, thecytosines originally present on the sequenced reads were recovered basedon the positional information kept during the in silico conversion. Therecovered cytosines among the CpG dinucleotides were scored asmethylated. Thymines among the CpG dinucleotides were scored asunmethylated. The unmethylated lambda DNA included during librarypreparation served as an internal control for estimating the efficiencyof sodium bisulfite modification. All cytosines on the lambda DNA shouldhave been converted to thymines if the bisulfite conversion efficiencywas 100%.

XIII. Summary

With the use of embodiments described herein, one could screen, detect,monitor or prognosticate cancer noninvasively using for example theplasma of a subject. One could also carry out prenatal screening,diagnosis, investigation or monitoring of a fetus by deducing themethylation profile of fetal DNA from maternal plasma. To illustrate thepower of the approach, we showed that information that wasconventionally obtained via the study of placental tissues could beassessed directly from maternal plasma. For example, the imprintingstatus of gene loci, identification of loci with differentialmethylation between the fetal and maternal DNA and the gestationalvariation in the methylation profile of gene loci were achieved throughthe direct analysis of maternal plasma DNA. The major advantage of ourapproach is that the fetal methylome could be assessed comprehensivelyduring pregnancy without disruption to the pregnancy or the need forinvasive sampling of fetal tissues. Given the known association betweenaltered DNA methylation status and the many pregnancy-associatedconditions, the approach described in this study can serve as animportant tool for investigating the pathophysiology of and theidentification of biomarkers for those conditions. By focusing on theimprinted loci, we showed that both the paternally-transmitted as wellas the maternally-transmitted fetal methylation profiles could beassessed from maternal plasma. This approach may potentially be usefulfor the investigation of imprinting diseases. Embodiments can also beapplied directly for the prenatal assessment of fetal orpregnancy-associated diseases.

We have demonstrated that genome-wide bisulfite sequencing can beapplied to investigate the DNA methylation profile of placental tissues.There are approximately 28M CpG sites in the human genome (C Clark etal. 2012 PLoS One; 7: e50233). Our bisulfite sequencing data of the CVSand term placental tissue sample covered more than 80% of the CpGs. Thisrepresents a substantially broader coverage than those achievable usingother high-throughput platforms. For example, the Illumina InfiniumHumanMethylation 27K beadchip array that was used in a previous study onplacental tissues (T Chu et al. 2011 PLoS One; 6: e14723) only covered0.1% of the CpGs in the genome. The Illumina Infinium HumanMethylation450K beadchip array that was available more recently only covered 1.7%of the CpGs (C Clark et al. 2012 PLoS One; 7: e50233). Because the MPSapproach is free from restrictions related to probe design,hybridization efficiency or strength of antibody capture, CpGs within orbeyond CpG islands and in most sequence contexts could be assessed.

XIV. Computer System

Any of the computer systems mentioned herein may utilize any suitablenumber of subsystems. Examples of such subsystems are shown in FIG. 33in computer apparatus 3300. In some embodiments, a computer systemincludes a single computer apparatus, where the subsystems can be thecomponents of the computer apparatus. In other embodiments, a computersystem can include multiple computer apparatuses, each being asubsystem, with internal components.

The subsystems shown in FIG. 33 are interconnected via a system bus3375. Additional subsystems such as a printer 3374, keyboard 3378,storage device(s) 3379, monitor 3376, which is coupled to displayadapter 3382, and others are shown. Peripherals and input/output (I/O)devices, which couple to I/O controller 3371, can be connected to thecomputer system by any number of means known in the art, such as serialport 3377. For example, serial port 3377 or external interface 3381(e.g. Ethernet, Wi-Fi, etc.) can be used to connect computer system 3300to a wide area network such as the Internet, a mouse input device, or ascanner. The interconnection via system bus 3375 allows the centralprocessor 3373 to communicate with each subsystem and to control theexecution of instructions from system memory 3372 or the storagedevice(s) 3379 (e.g., a fixed disk), as well as the exchange ofinformation between subsystems. The system memory 3372 and/or thestorage device(s) 3379 may embody a computer readable medium. Any of thevalues mentioned herein can be output from one component to anothercomponent and can be output to the user.

A computer system can include a plurality of the same components orsubsystems, e.g., connected together by external interface 3381 or by aninternal interface. In some embodiments, computer systems, subsystem, orapparatuses can communicate over a network. In such instances, onecomputer can be considered a client and another computer a server, whereeach can be part of a same computer system. A client and a server caneach include multiple systems, subsystems, or components.

It should be understood that any of the embodiments of the presentinvention can be implemented in the form of control logic using hardware(e.g. an application specific integrated circuit or field programmablegate array) and/or using computer software with a generally programmableprocessor in a modular or integrated manner. As user herein, a processorincludes a multi-core processor on a same integrated chip, or multipleprocessing units on a single circuit board or networked. Based on thedisclosure and teachings provided herein, a person of ordinary skill inthe art will know and appreciate other ways and/or methods to implementembodiments of the present invention using hardware and a combination ofhardware and software.

Any of the software components or functions described in thisapplication may be implemented as software code to be executed by aprocessor using any suitable computer language such as, for example,Java, C++ or Perl using, for example, conventional or object-orientedtechniques. The software code may be stored as a series of instructionsor commands on a computer readable medium for storage and/ortransmission, suitable media include random access memory (RAM), a readonly memory (ROM), a magnetic medium such as a hard-drive or a floppydisk, or an optical medium such as a compact disk (CD) or DVD (digitalversatile disk), flash memory, and the like. The computer readablemedium may be any combination of such storage or transmission devices.

Such programs may also be encoded and transmitted using carrier signalsadapted for transmission via wired, optical, and/or wireless networksconforming to a variety of protocols, including the Internet. As such, acomputer readable medium according to an embodiment of the presentinvention may be created using a data signal encoded with such programs.Computer readable media encoded with the program code may be packagedwith a compatible device or provided separately from other devices(e.g., via Internet download). Any such computer readable medium mayreside on or within a single computer program product (e.g. a harddrive, a CD, or an entire computer system), and may be present on orwithin different computer program products within a system or network. Acomputer system may include a monitor, printer, or other suitabledisplay for providing any of the results mentioned herein to a user.

Any of the methods described herein may be totally or partiallyperformed with a computer system including one or more processors, whichcan be configured to perform the steps. Thus, embodiments can bedirected to computer systems configured to perform the steps of any ofthe methods described herein, potentially with different componentsperforming a respective steps or a respective group of steps. Althoughpresented as numbered steps, steps of methods herein can be performed ata same time or in a different order. Additionally, portions of thesesteps may be used with portions of other steps from other methods. Also,all or portions of a step may be optional. Additionally, any of thesteps of any of the methods can be performed with modules, circuits, orother means for performing these steps.

The specific details of particular embodiments may be combined in anysuitable manner without departing from the spirit and scope ofembodiments of the invention. However, other embodiments of theinvention may be directed to specific embodiments relating to eachindividual aspect, or specific combinations of these individual aspects.

The above description of exemplary embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdescribed, and many modifications and variations are possible in lightof the teaching above. The embodiments were chosen and described inorder to best explain the principles of the invention and its practicalapplications to thereby enable others skilled in the art to best utilizethe invention in various embodiments and with various modifications asare suited to the particular use contemplated.

A recitation of “a”, “an” or “the” is intended to mean “one or more”unless specifically indicated to the contrary.

All patents, patent applications, publications, and descriptionsmentioned here are incorporated by reference in their entirety for allpurposes. None is admitted to be prior art.

TABLE S2A List of 100 most hypermethylated regions identified from firsttrimester chorionic villus sample and maternal blood cells. Maternalblood Methylation Chromosome Start End Size (bp) cells CVS P-valuesDifference chr13 113063600 113064100 500 0.009 0.9 3.67E−15 0.891 chr636279700 36280200 500 0.0068 0.8957 2.39E−22 0.8889 chr16 6687600066876500 500 0.0327 0.9211 3.82E−21 0.8884 chr10 163500 164000 5000.0195 0.9034 3.60E−35 0.8839 chr9 3518300 3518800 500 0.0263 0.90451.32E−26 0.8782 chr12 31877100 31877600 500 0.007 0.8784 3.08E−22 0.8714chr22 37477400 37478400 1000 0.0152 0.8848 0.00E+00 0.8696 chr4148940500 148941000 500 0.0055 0.8717 4.40E−29 0.8662 chr5 131836300131836800 500 0.075 0.9403 1.54E−10 0.8653 chr17 26661700 26663600 19000.0187 0.875 2.95E−38 0.8563 chr2 105758600 105759600 1000 0.0305 0.88281.19E−53 0.8523 chr22 39188800 39189800 1000 0 0.8514 2.05E−46 0.8514chr3 153443900 153444900 1000 0.0436 0.8945 5.43E−34 0.8509 chr625149600 25150600 1000 0.0135 0.8632 0.00E+00 0.8497 chr5 9829680098297300 500 0.0432 0.8925 4.97E−23 0.8493 chr7 150679900 150680400 5000.0496 0.8944 6.50E−17 0.8448 chr7 107563100 107563600 500 0.0495 0.88959.58E−26 0.84 chr7 37348300 37349300 1000 0.0012 0.8409 0.00E+00 0.8397chr14 58837800 58838300 500 0.0097 0.848 3.35E−16 0.8383 chr6 119238100119238600 500 0.0899 0.928 2.38E−19 0.8381 chr15 93669900 93670400 5000.0753 0.913 2.19E−10 0.8377 chr17 26669200 26670200 1000 0.0221 0.8591.44E−29 0.8369 chr2 88108100 88108600 500 0.075 0.9109 3.55E−17 0.8359chr13 98363800 98364300 500 0.11 0.9457 1.28E−11 0.8357 chr16 6694800066948500 500 0.0331 0.8685 0.00E+00 0.8354 chr6 42098000 42098500 5000.0484 0.8835 3.73E−16 0.8351 chr3 129876000 129876500 500 0.0565 0.88978.81E−17 0.8332 chr3 142700300 142700800 500 0.0063 0.8393 2.59E−220.833 chr8 145883800 145884300 500 0.0392 0.872 0.00E+00 0.8328 chr108320700 8321200 500 0.0566 0.8871 9.40E−09 0.8305 chr3 120438100120438600 500 0.102 0.9292 7.09E−16 0.8272 chr3 173792600 173793100 5000.0182 0.8453 2.84E−39 0.8271 chr17 40320700 40321200 500 0.0539 0.87886.50E−30 0.8249 chr15 72076200 72076700 500 0.0299 0.8525 6.48E−100.8226 chr16 29663900 29665400 1500 0.0081 0.8305 0.00E+00 0.8224 chr1166961100 66962100 1000 0.0489 0.8712 0.00E+00 0.8223 chr9 2708310027084100 1000 0.097 0.9177 2.37E−55 0.8207 chr9 111249600 111250100 5000.0613 0.8795 1.99E−20 0.8182 chr14 101412400 101412900 500 0 0.81678.26E−32 0.8167 chr1 242549200 242549700 500 0 0.8155 3.50E−21 0.8155chr8 38642800 38643300 500 0.0191 0.8346 3.22E−41 0.8155 chr4 8589360085894100 500 0.0394 0.8533 1.45E−15 0.8139 chr5 142368600 142369100 5000.0385 0.8523 1.18E−18 0.8138 chr8 130969500 130970000 500 0.069 0.88242.42E−24 0.8134 chr2 196783900 196784400 500 0 0.8123 3.63E−40 0.8123chr16 49258100 49258600 500 0.0733 0.8851 4.29E−18 0.8118 chr1 232601200232601700 500 0.0594 0.8707 1.73E−13 0.8113 chr1 109039500 109040000 5000.0366 0.8471 1.07E−11 0.8105 chr17 59491300 59491800 500 0.0662 0.87582.15E−17 0.8096 chr21 42194100 42194600 500 0.11 0.9182 1.61E−12 0.8082chr9 116174500 116175500 1000 0.0062 0.8132 1.98E−60 0.807 chr1573429200 73429700 500 0 0.8066 9.81E−33 0.8066 chr6 157462800 157463300500 0.0758 0.8819 7.94E−16 0.8061 chr3 16858500 16859500 1000 0.00210.8068 4.76E−08 0.8047 chr9 96662800 96663300 500 0.0614 0.8651 6.79E−280.8037 chr9 88143000 88143500 500 0.1538 0.9559 7.43E−09 0.8021 chr1916090000 16091000 1000 0.0899 0.8904 1.60E−53 0.8005 chr15 2943630029437300 1000 0.0553 0.8556 1.18E−80 0.8003 chr11 77816100 77816600 5000.1069 0.9068 2.31E−17 0.7999 chr10 30346800 30347300 500 0.1212 0.92117.48E−07 0.7999 chr1 89510300 89511300 1000 0.0203 0.8191 3.53E−770.7988 chr3 125986100 125986600 500 0.1686 0.9674 5.24E−22 0.7988 chr1960162800 60163300 500 0.0127 0.8113 9.99E−19 0.7986 chr16 7365590073656900 1000 0.082 0.8806 4.48E−41 0.7986 chr16 30104300 30105800 15000.0298 0.8282 0.00E+00 0.7984 chr10 118642400 118642900 500 0.05880.8571 8.63E−11 0.7983 chr16 4495000 4496000 1000 0.0632 0.8615 2.27E−440.7983 chr1 2048300 2048800 500 0.0309 0.8289 1.19E−80 0.798 chr2136481800 136482800 1000 0.0554 0.8533 8.50E−48 0.7979 chr10 2995920029959700 500 0.1429 0.94 2.60E−08 0.7971 chr6 139642400 139642900 5000.0618 0.8585 2.16E−29 0.7967 chr14 69825300 69825800 500 0.0654 0.86156.85E−14 0.7961 chr8 49739700 49740200 500 0.0324 0.828 2.88E−30 0.7956chr17 42205700 42206200 500 0.057 0.852 2.11E−30 0.795 chr4 7744530077445800 500 0.0442 0.8377 1.79E−35 0.7935 chr17 53762700 53766300 36000.0003 0.7926 0.00E+00 0.7923 chr17 44269900 44270400 500 0.026 0.81823.49E−21 0.7922 chr6 42462700 42463200 500 0.0761 0.8678 4.74E−22 0.7917chr2 23396200 23396700 500 0.0333 0.8235 1.25E−14 0.7902 chr9 100921100100921600 500 0.0244 0.814 3.32E−21 0.7896 chr7 74016100 74016600 5000.1442 0.9333 6.74E−10 0.7891 chr6 157879000 157879500 500 0.133 0.92196.36E−17 0.7889 chr3 3189400 3190400 1000 0.0693 0.8571 1.38E−24 0.7878chr16 29581500 29584500 3000 0.0081 0.7956 0.00E+00 0.7875 chr1742201800 42202800 1000 0.0884 0.8751 0.00E+00 0.7867 chr11 9425700094257500 500 0.1122 0.8986 4.29E−10 0.7864 chr10 14741600 14742100 5000.0139 0.8 1.73E−20 0.7861 chr21 33826900 33827400 500 0.0879 0.87392.81E−11 0.786 chr4 130057200 130057700 500 0.0893 0.875 1.76E−13 0.7857chr21 35343400 35343900 500 0 0.7853 7.43E−18 0.7853 chr12 105372800105373300 500 0.0923 0.8767 8.67E−22 0.7844 chr5 10799800 10800300 5000.1429 0.9263 8.21E−17 0.7834 chr5 16753100 16753600 500 0.041 0.82411.40E−15 0.7831 chr3 135746000 135746500 500 0.1429 0.9259 2.86E−090.783 chr6 53708300 53708800 500 0.0412 0.8235 2.74E−31 0.7823 chr2128122900 128123400 500 0.0634 0.8455 4.82E−21 0.7821 chr5 150574200150574700 500 0.0876 0.8696 1.56E−20 0.782 chr16 84326000 84327000 10000.1071 0.8891 3.58E−61 0.782 chr1 26744500 26745500 1000 0.0336 0.81520.00E+00 0.7816 chr2 234882000 234882500 500 0.0392 0.819 7.63E−140.7798

TABLE S2B List of 100 most hypomethylated regions identified from firsttrimester chorionic villus sample and maternal blood cells. Maternalblood Methylation Chromosome Start End Size (bp) cells CVS P-valuesDifference chr18 12217500 12218500 1000 0.9873 0 3.05E−25 0.9873 chr1722885400 22885900 500 0.9714 0.0161 8.92E−12 0.9553 chr3 184827100184827600 500 0.9875 0.033 4.79E−16 0.9545 chr5 148968300 148968800 5000.98 0.0426 6.70E−09 0.9374 chr10 104794500 104795000 500 0.973 0.03859.33E−10 0.9345 chr4 84977900 84978400 500 0.9643 0.0417 2.98E−08 0.9226chr3 180395300 180395800 500 0.9877 0.0667 6.72E−08 0.921 chr2 138908300138908800 500 0.939 0.0208 1.10E−16 0.9182 chr6 139873100 139873600 5000.9667 0.0526 1.29E−07 0.914 chr8 59604700 59605200 500 0.9468 0.0332.88E−14 0.9138 chr6 167622300 167622800 500 0.9452 0.0316 3.86E−140.9136 chr3 175701300 175701800 500 0.9846 0.0735 7.43E−10 0.9111 chr1359246400 59246900 500 0.9402 0.0313 2.31E−11 0.9089 chr12 7126360071264100 500 0.9296 0.0213 1.08E−08 0.9083 chr5 39459400 39459900 5000.9219 0.014 5.01E−22 0.9079 chr17 24904700 24905200 500 0.9161 0.00925.04E−35 0.9069 chr12 31889900 31890400 500 0.9524 0.0465 6.78E−130.9059 chr3 152897800 152898300 500 0.9402 0.0345 1.70E−17 0.9057 chr140378700 40379200 500 0.9565 0.0526 3.31E−09 0.9039 chr12 4397930043979800 500 0.952 0.05 6.68E−13 0.902 chr18 1395900 1397400 1500 0.93080.0293 0.00E+00 0.9015 chr1 223482900 223483400 500 0.9579 0.05753.36E−24 0.9004 chr9 130357000 130357500 500 0.9282 0.0286 9.19E−130.8996 chr3 72878300 72878800 500 0.9612 0.0625 8.20E−14 0.8987 chr784347200 84348700 1500 0.9401 0.0418 0.00E+00 0.8983 chr15 3731750037318000 500 0.9358 0.0385 7.58E−14 0.8973 chr8 42528600 42529100 5000.9302 0.0337 1.73E−14 0.8965 chr6 134914000 134914500 500 0.9037 0.00764.84E−21 0.8961 chr13 56207100 56208100 1000 0.9184 0.0245 0.00E+000.894 chr2 209074000 209074500 500 0.9309 0.037 6.13E−27 0.8938 chr1274021100 74022100 1000 0.9513 0.058 0.00E+00 0.8933 chr4 118939300118939800 500 0.9192 0.0276 6.58E−27 0.8916 chr5 12626600 12628600 20000.9266 0.0355 0.00E+00 0.8911 chr5 105517300 105518300 1000 0.927 0.03590.00E+00 0.891 chr12 70056300 70057300 1000 0.9488 0.0609 0.00E+00 0.888chr6 153238200 153239200 1000 0.9123 0.0244 0.00E+00 0.8879 chr1760374800 60375300 500 0.9655 0.0777 3.64E−14 0.8878 chr14 6827270068273200 500 0.9389 0.0523 1.23E−22 0.8866 chr19 54533800 54534800 10000.9117 0.0262 0.00E+00 0.8855 chr12 15392200 15393200 1000 0.9307 0.04570.00E+00 0.885 chr1 212517400 212517900 500 0.9266 0.0417 9.81E−120.8849 chr10 49344400 49345400 1000 0.9422 0.0579 0.00E+00 0.8844 chr347410400 47410900 500 0.9213 0.0381 5.59E−16 0.8832 chr3 879500 880000500 0.9455 0.0625 8.06E−06 0.883 chr2 31572400 31573400 1000 0.91760.0357 0.00E+00 0.8819 chr1 89131200 89131700 500 0.9314 0.0498 5.15E−700.8816 chr8 94832000 94832500 500 0.9156 0.0351 2.16E−65 0.8805 chr714008300 14009800 1500 0.9349 0.0545 0.00E+00 0.8804 chr12 1297130012972300 1000 0.9361 0.0559 0.00E+00 0.8802 chr5 43114700 43115200 5000.9638 0.0842 1.79E−13 0.8796 chr11 107872400 107872900 500 0.94720.0677 2.31E−32 0.8794 chr8 49757600 49758100 500 0.9048 0.0269 3.15E−520.8779 chr13 33106400 33106900 500 0.9384 0.0606 9.54E−15 0.8778 chr3190658800 190659300 500 0.9388 0.0617 2.71E−22 0.877 chr1 181508000181508500 500 0.9259 0.0495 3.78E−15 0.8764 chr1 180436900 180437400 5000.9412 0.0652 2.36E−13 0.876 chr6 122642800 122643800 1000 0.9218 0.04580.00E+00 0.8759 chr5 166429300 166429800 500 0.9551 0.08 5.26E−05 0.8751chr12 14972900 14973400 500 0.9483 0.0733 2.10E−18 0.8749 chr5 123933900123934400 500 0.943 0.0683 1.12E−39 0.8746 chr2 15969400 15970400 10000.8939 0.0196 9.43E−46 0.8743 chr3 167635200 167636200 1000 0.93630.0625 9.03E−41 0.8738 chr5 159442700 159443200 500 0.9174 0.0446.27E−14 0.8734 chr4 48027200 48027700 500 0.9839 0.1111 8.89E−06 0.8728chr6 140071500 140072000 500 0.9234 0.0506 4.39E−33 0.8728 chr1022356300 22356800 500 0.9548 0.0822 1.04E−18 0.8726 chr8 6100730061007800 500 0.9197 0.0476 1.24E−15 0.8721 chr11 95463500 95464000 5000.9348 0.0629 1.16E−20 0.8718 chr2 216399800 216400300 500 0.938 0.06675.98E−06 0.8713 chr18 57359700 57360200 500 0.9293 0.0584 1.77E−19 0.871chr3 102734400 102734900 500 0.8917 0.0207 7.94E−22 0.871 chr1 173605700173606200 500 0.96 0.0891 5.46E−13 0.8709 chr2 86993700 86995700 20000.8965 0.0261 0.00E+00 0.8704 chr3 162621100 162621600 500 0.9226 0.05267.89E−38 0.8699 chr12 10144800 10145300 500 0.929 0.0598 3.45E−17 0.8691chr3 113855100 113855600 500 0.9667 0.0982 3.97E−14 0.8685 chr2156958200 156959200 1000 0.9252 0.0571 8.89E−50 0.8681 chr2 5577500055776000 1000 0.9159 0.0483 0.00E+00 0.8676 chr6 124898400 124898900 5000.8987 0.0313 1.91E−15 0.8675 chr5 42003700 42004700 1000 0.9262 0.05880.00E+00 0.8674 chr3 24162200 24162700 500 0.883 0.0161 1.75E−27 0.8668chr6 35394000 35395000 1000 0.9204 0.0539 0.00E+00 0.8665 chr17 84518008453300 1500 0.9376 0.0714 0.00E+00 0.8662 chr14 53487700 53488700 10000.9013 0.0353 0.00E+00 0.866 chr7 98572800 98573300 500 0.9651 0.09958.37E−26 0.8656 chr6 52298700 52299200 500 0.9427 0.0772 1.31E−28 0.8655chr6 159047900 159048400 500 0.908 0.0426 3.34E−08 0.8655 chr14 2215260022153100 500 0.9085 0.0435 4.43E−17 0.865 chr12 103285000 103285500 5000.9321 0.0674 0.00E+00 0.8647 chr7 43302200 43302700 500 0.968 0.10376.40E−16 0.8643 chr14 22247400 22247900 500 0.9804 0.1163 3.83E−060.8641 chr2 66780900 66781400 500 0.9355 0.0714 8.37E−09 0.8641 chr1297393000 97393500 500 0.9045 0.0408 3.46E−21 0.8637 chr5 162797900162798900 1000 0.9271 0.0635 1.75E−57 0.8636 chr2 83598400 83599400 10000.9354 0.0719 0.00E+00 0.8635 chr11 111358800 111359300 500 0.91560.0523 4.15E−24 0.8632 chr11 104891100 104892600 1500 0.9164 0.05330.00E+00 0.863 chr1 184583600 184584100 500 0.9647 0.1026 3.02E−130.8621 chr5 132350500 132351500 1000 0.9042 0.0426 1.86E−36 0.8616 chr553268300 53268800 500 0.972 0.1111 4.76E−16 0.8609

TABLE S2C List of 100 most hypermethylated regions identified from thirdtrimester placental tissue and maternal blood cells. Maternal blood TermMethylation Chromosome Start End Size (bp) cells placenta P-valuesDifference chr4 78129700 78130200 500 0.0488 0.9747 3.97E−33 0.926 chr5131467400 131467900 500 0.0213 0.9275 7.10E−27 0.9063 chr17 2666170026663600 1900 0.0187 0.9226 1.79E−41 0.9039 chr4 148940500 148941000 5000.0055 0.9079 1.82E−29 0.9024 chr9 100921100 100921600 500 0.0244 0.92421.38E−25 0.8998 chr6 137114200 137114700 500 0 0.8934 8.87E−14 0.8934chr3 173792600 173793100 500 0.0182 0.9091 1.70E−42 0.8908 chr5 9829680098297300 500 0.0432 0.9333 2.58E−23 0.8901 chr12 44898000 44898500 500 00.8889 4.47E−11 0.8889 chr3 197328900 197329400 500 0.0169 0.90485.55E−10 0.8878 chr8 49739700 49740200 500 0.0324 0.9194 5.71E−34 0.887chr12 122279300 122279800 500 0.0135 0.8969 3.46E−21 0.8834 chr1743092200 43092700 500 0 0.8824 4.34E−10 0.8824 chr7 107563100 107563600500 0.0495 0.931 1.05E−28 0.8815 chr11 72543200 72543700 500 0.03770.9167 2.94E−09 0.8789 chr14 58837800 58838300 500 0.0097 0.886 9.16E−180.8763 chr3 153443900 153444900 1000 0.0436 0.9197 6.24E−39 0.876 chr316953200 16953700 500 0.0896 0.9655 6.78E−09 0.876 chr17 4220570042206200 500 0.057 0.933 1.13E−31 0.8759 chr6 53217600 53218100 5000.0818 0.9571 1.54E−19 0.8754 chr3 112749000 112749500 500 0.0403 0.91544.11E−22 0.8752 chr8 22453700 22454200 500 0.003 0.8765 1.64E−50 0.8735chr1 162860900 162861400 500 0.023 0.8932 8.37E−14 0.8702 chr6 3627970036280200 500 0.0068 0.8762 1.14E−21 0.8694 chr5 80962500 80963000 500 00.8679 2.08E−15 0.8679 chr16 11312500 11313000 500 0 0.8679 2.14E−100.8679 chr16 29663900 29665400 1500 0.0081 0.8759 0.00E+00 0.8678 chr3120438100 120438600 500 0.102 0.9639 2.98E−15 0.8618 chr8 134157000134157500 500 0.0625 0.9219 6.10E−20 0.8594 chr6 42620900 42621400 500 00.8571 5.68E−08 0.8571 chr5 131836300 131836800 500 0.075 0.93151.26E−10 0.8565 chr14 60290000 60290500 500 0 0.8544 5.63E−14 0.8544chr6 42850300 42851300 1000 0.0676 0.9211 2.38E−24 0.8534 chr8 2897410028974600 500 0.0394 0.8927 2.03E−51 0.8533 chr22 22368500 22369000 5000.0248 0.8778 1.18E−70 0.8529 chr14 69825300 69825800 500 0.0654 0.91742.73E−14 0.852 chr3 142700300 142700800 500 0.0063 0.8582 2.56E−230.8519 chr17 59491300 59491800 500 0.0662 0.9175 1.81E−16 0.8513 chr1530881700 30882200 500 0.0493 0.8995 2.38E−26 0.8502 chr15 9149630091496800 500 0 0.85 3.13E−17 0.85 chr17 18745300 18745800 500 0.02940.8775 3.47E−51 0.848 chr15 29436500 29437000 500 0.0336 0.8811 2.62E−660.8476 chr2 217795300 217795800 500 0 0.8472 1.78E−22 0.8472 chr1116328100 16328600 500 0.0278 0.875 3.43E−11 0.8472 chr13 113063500113064000 500 0.0102 0.8571 1.82E−15 0.8469 chr5 40472400 40472900 5000.0197 0.8664 7.54E−35 0.8467 chr1 242549200 242549700 500 0 0.84628.53E−23 0.8462 chr11 58099100 58099600 500 0.0162 0.8612 4.45E−35 0.845chr9 16020400 16020900 500 0.0132 0.8555 8.05E−23 0.8423 chr8 3755070037551200 500 0.0093 0.8512 1.11E−16 0.8419 chr5 75722400 75722900 5000.1215 0.9627 5.97E−23 0.8411 chr19 60454700 60455200 500 0.0316 0.87222.44E−62 0.8405 chr4 99587100 99587600 500 0.0128 0.8526 1.49E−12 0.8398chr6 25149600 25150600 1000 0.0135 0.8514 0.00E+00 0.8379 chr1 3206520032065700 500 0 0.8371 1.09E−44 0.8371 chr7 5337200 5337700 500 0.07270.9098 2.18E−14 0.8371 chr17 44269900 44270400 500 0.026 0.8621 3.94E−220.8361 chr1 36180800 36181300 500 0.0714 0.9067 1.23E−09 0.8352 chr1810472700 10473700 1000 0.0713 0.9064 9.35E−70 0.8351 chr5 350000 350500500 0.0297 0.8643 1.49E−16 0.8346 chr2 136481800 136482800 1000 0.05540.8887 1.87E−52 0.8332 chr4 89241100 89241600 500 0.1091 0.9423 1.05E−120.8332 chr17 40320700 40321200 500 0.0539 0.8859 6.94E−31 0.832 chr7133897200 133897700 500 0.0769 0.9077 1.64E−24 0.8308 chr8 9806060098061100 500 0.0741 0.9048 3.11E−07 0.8307 chr8 134141500 134142000 5000 0.829 2.77E−58 0.829 chr14 80250600 80251100 500 0.0839 0.91222.05E−18 0.8283 chr2 100730900 100731400 500 0.0787 0.9067 4.85E−110.828 chr2 88108100 88108600 500 0.075 0.901 2.10E−16 0.826 chr1916338500 16339500 1000 0.0011 0.8259 0.00E+00 0.8247 chr5 141791900141792900 1000 0.0225 0.8467 0.00E+00 0.8243 chr11 116227400 116227900500 0 0.8242 1.01E−17 0.8242 chr22 48705500 48706000 500 0.0649 0.88914.00E−76 0.8242 chr9 3518300 3518800 500 0.0263 0.8493 6.66E−25 0.823chr11 16791000 16791500 500 0.1095 0.9322 1.05E−22 0.8228 chr3 135746000135746500 500 0.1429 0.9651 7.63E−10 0.8223 chr1 19323400 19323900 5000.0411 0.8624 2.36E−20 0.8213 chr9 96662800 96663300 500 0.0614 0.88261.07E−28 0.8212 chr7 37348300 37349300 1000 0.0012 0.821 0.00E+00 0.8198chr2 234882000 234882500 500 0.0392 0.8591 5.36E−15 0.8198 chr6 4469400044694500 500 0.1024 0.9222 5.68E−19 0.8198 chr17 18320500 18321000 500 00.8197 2.78E−39 0.8197 chr22 28992000 28994000 2000 0.0012 0.81950.00E+00 0.8183 chr17 53762700 53766300 3600 0.0003 0.8179 0.00E+000.8176 chr1 114215500 114216000 500 0 0.8169 2.24E−20 0.8169 chr613381700 13382700 1000 0.0037 0.8206 1.03E−40 0.8169 chr5 1704540017045900 500 0.0235 0.84 3.24E−13 0.8165 chr12 110924300 110924800 5000.0855 0.9016 1.01E−18 0.816 chr1 200499800 200500300 500 0.011 0.82699.73E−24 0.8159 chr4 8311000 8311500 500 0.053 0.8687 7.82E−18 0.8157chr8 6535300 6535800 500 0.0667 0.8824 5.25E−09 0.8157 chr6 4246270042463200 500 0.0761 0.8919 5.78E−23 0.8157 chr1 91969900 91970400 5000.0172 0.8325 5.23E−18 0.8152 chr2 105758600 105759600 1000 0.03050.8455 5.15E−52 0.815 chr21 37538500 37539000 500 0.1595 0.9745 8.81E−160.815 chr9 92953000 92953500 500 0.0189 0.8333 1.88E−15 0.8145 chr1630104400 30105900 1500 0.0505 0.8636 0.00E+00 0.8131 chr1 234184400234185400 1000 0.0346 0.8477 9.66E−31 0.813 chr8 19116400 19116900 500 00.8125 9.33E−11 0.8125 chr4 141194300 141195300 1000 0.0865 0.8995.46E−30 0.8125

TABLE S2D List of 100 most hypomethylated regions identified from thirdtrimester placental tissue and maternal blood cells. Maternal blood TermMethylation Chromosome Start End Size (bp) cells placenta P-valuesDifference chr9 40380300 40380800 500 0.9667 0 1.13E−06 0.9667 chr131769200 31769700 500 0.9548 0.0256 5.57E−25 0.9291 chr18 1221760012218100 500 0.9873 0.0602 1.63E−19 0.9271 chr20 19704400 19704900 5000.9426 0.018 4.34E−18 0.9246 chr15 37317500 37318000 500 0.9358 0.01321.90E−25 0.9226 chrX 83368400 83368900 500 0.913 0 3.15E−07 0.913 chr1127549100 27549600 500 0.9224 0.0123 3.92E−24 0.9101 chr18 5814150058142000 500 0.9737 0.0645 1.07E−09 0.9092 chr1 159897000 159897500 5000.9067 0 2.53E−16 0.9067 chr7 84347200 84348700 1500 0.9401 0.04070.00E+00 0.8994 chr2 216916100 216916600 500 0.9695 0.0714 2.13E−160.8981 chr7 144200000 144200500 500 0.9294 0.0317 1.24E−10 0.8977 chr1241331600 241332100 500 0.9198 0.0227 0.00E+00 0.8971 chr7 123190000123191000 1000 0.9341 0.0384 0.00E+00 0.8957 chr5 12626600 12628600 20000.9266 0.0321 0.00E+00 0.8944 chr12 12971300 12972300 1000 0.9361 0.04380.00E+00 0.8923 chr22 20936500 20937000 500 0.9528 0.0606 1.87E−060.8922 chr13 31321900 31322400 500 0.9231 0.0313 1.43E−06 0.8918 chr2221701500 21702000 500 0.9579 0.0667 1.30E−09 0.8912 chr10 104794400104794900 500 1 0.1111 6.10E−09 0.8889 chr7 21835800 21836300 500 0.91560.0267 3.85E−13 0.8889 chr10 16134800 16135300 500 0.95 0.0635 4.79E−100.8865 chr3 47410400 47410900 500 0.9213 0.0357 6.63E−17 0.8855 chr1049344400 49345400 1000 0.9422 0.0571 0.00E+00 0.8851 chr2 209073900209074400 500 0.9196 0.0353 1.63E−22 0.8843 chr1 89131200 89131700 5000.9314 0.0472 1.05E−75 0.8842 chr3 167118500 167119500 1000 0.93650.0527 0.00E+00 0.8838 chr18 1395900 1397400 1500 0.9308 0.0472 0.00E+000.8836 chr2 59670300 59670800 500 0.9433 0.0599 5.09E−23 0.8834 chr1428368900 28369400 500 0.9446 0.0619 5.03E−64 0.8827 chr3 126028800126029300 500 0.9379 0.0556 1.83E−20 0.8823 chr9 69378900 69379900 10000.8816 0 6.02E−51 0.8816 chr5 105517300 105518300 1000 0.927 0.04610.00E+00 0.8808 chr2 31572400 31573400 1000 0.9176 0.037 0.00E+00 0.8806chr5 42003700 42004700 1000 0.9262 0.0462 0.00E+00 0.88 chr14 9471830094718800 500 0.9548 0.0764 6.67E−19 0.8784 chr19 56417800 56418300 5000.9615 0.0833 1.71E−06 0.8782 chr2 70183000 70183500 500 0.9694 0.09149.49E−39 0.878 chr4 118939300 118939800 500 0.9192 0.0412 2.20E−34 0.878chr13 59246400 59246900 500 0.9402 0.0633 5.40E−12 0.8769 chr12 7402110074022100 1000 0.9513 0.0752 0.00E+00 0.8761 chr2 173432500 173433000 5000.9529 0.0778 5.39E−12 0.8752 chr16 24004400 24004900 500 0.9239 0.04883.25E−23 0.8751 chr13 27596300 27597300 1000 0.9538 0.0795 0.00E+000.8743 chr15 88904300 88904800 500 0.9212 0.0481 7.69E−27 0.8731 chr1812720200 12721200 1000 0.9346 0.0618 0.00E+00 0.8728 chr15 6097590060976900 1000 0.9311 0.0587 0.00E+00 0.8724 chr21 39630100 39631100 10000.9423 0.07 4.68E−43 0.8723 chr5 123933900 123934400 500 0.943 0.07072.60E−38 0.8722 chr8 77382600 77383600 1000 0.9117 0.0395 0.00E+000.8722 chr21 32238800 32239300 500 0.9396 0.0677 1.28E−18 0.8719 chr5175019600 175020100 500 0.9542 0.0828 4.34E−20 0.8714 chr8 134437400134438400 1000 0.9083 0.037 4.79E−29 0.8713 chr5 69668800 69669300 5000.9194 0.0492 2.88E−09 0.8702 chr1 60877900 60878900 1000 0.9378 0.0680.00E+00 0.8698 chr16 80650400 80650900 500 0.9309 0.0611 2.49E−320.8698 chr18 59388800 59389300 500 0.9706 0.1008 4.84E−15 0.8697 chr215969400 15970400 1000 0.8939 0.0244 3.07E−52 0.8695 chr13 5620710056208100 1000 0.9184 0.0505 0.00E+00 0.868 chr3 180395300 180395800 5000.9877 0.12 1.73E−09 0.8677 chr6 153238200 153239200 1000 0.9123 0.04520.00E+00 0.8671 chr18 61635100 61635600 500 0.9268 0.06 2.77E−13 0.8668chr3 177562200 177563200 1000 0.9121 0.0455 0.00E+00 0.8666 chr4160368300 160370200 1900 0.9272 0.0606 0.00E+00 0.8665 chr6 144626900144627400 500 0.9114 0.046 2.10E−12 0.8654 chr16 59885500 59886500 10000.9407 0.0757 1.12E−62 0.865 chr1 55667100 55667600 500 0.9095 0.04468.62E−39 0.8649 chr2 83598300 83599300 1000 0.9366 0.0718 0.00E+000.8648 chr4 105135200 105136200 1000 0.913 0.0486 0.00E+00 0.8644 chr1432048400 32048900 500 0.9142 0.0499 5.43E−53 0.8643 chr1 223482700223483700 1000 0.9636 0.0997 2.69E−34 0.864 chr14 47487700 47488200 5000.915 0.0514 5.45E−33 0.8636 chr3 104515000 104515500 500 1 0.13731.08E−06 0.8627 chr7 14008300 14009800 1500 0.9349 0.0725 0.00E+000.8624 chr1 243134000 243135500 1500 0.9208 0.0588 0.00E+00 0.8619 chr1014156400 14156900 500 0.9105 0.0489 0.00E+00 0.8616 chr2 118616200118617200 1000 0.9178 0.0565 0.00E+00 0.8613 chr17 8455500 8456000 5000.8941 0.0331 1.94E−18 0.8611 chr12 15392200 15393200 1000 0.9307 0.06970.00E+00 0.861 chr8 81275900 81276900 1000 0.9291 0.0684 0.00E+00 0.8606chr1 234269300 234269800 500 0.9471 0.087 2.25E−25 0.8602 chr1 181970300181970800 500 0.9167 0.0566 2.15E−08 0.8601 chr2 55775000 55776000 10000.9159 0.0559 0.00E+00 0.8599 chr3 88338000 88339000 1000 0.8909 0.03110.00E+00 0.8598 chr5 140078700 140079200 500 0.8852 0.0253 0.00E+000.8598 chr21 16720900 16721400 500 0.9317 0.0721 1.38E−15 0.8596 chr11104891100 104892600 1500 0.9164 0.0569 0.00E+00 0.8595 chr1 184204700184205200 500 0.9194 0.0603 8.16E−16 0.859 chr6 160732500 160733000 5000.9191 0.0606 1.31E−10 0.8585 chr8 37134300 37134800 500 0.9151 0.05671.21E−26 0.8584 chr18 5869800 5870300 500 0.913 0.0548 1.21E−09 0.8582chr1 98448100 98448600 500 0.9574 0.1 2.08E−05 0.8574 chr3 152897800152898300 500 0.9402 0.0828 3.28E−18 0.8574 chr1 110304000 110304500 5000.9783 0.121 2.60E−18 0.8572 chr2 86993600 86995600 2000 0.8965 0.03950.00E+00 0.857 chr19 15428100 15430600 2500 0.9424 0.0862 0.00E+000.8563 chr13 75176800 75177800 1000 0.9258 0.0697 3.09E−47 0.8561 chr1324126700 24127200 500 0.9498 0.0938 1.57E−17 0.856 chr16 2823850028240500 2000 0.9427 0.0868 0.00E+00 0.8559 chr2 158079500 1580805001000 0.9199 0.0642 0.00E+00 0.8557

TABLE S3A List of the top 100 loci deduced to be hypermethylated fromthe first trimester maternal plasma bisulfite-sequencing data. Maternalblood CVS Chromosome Start End cells difference Methylation chr2239189067 39189863 0 0.8444 0.8444 chr17 53763065 53764027 0 0.79220.7922 chr7 41887694 41888212 0 0.7614 0.7614 chr2 1.14E+08 1.14E+08 00.751 0.751 chr12 25096242 25097206 0 0.7098 0.7098 chr1 6657410466574793 0 0.7025 0.7025 chr6 11489985 11490755 0 0.7004 0.7004 chr61.07E+08 1.07E+08 0 0.6978 0.6978 chr10 30858286 30858871 0 0.66930.6693 chr17 21131574 21132167 0 0.6496 0.6496 chr18 13454740 13455292 00.5468 0.5468 chr16 11298755 11299326 0 0.5373 0.5373 chr2 1.75E+081.75E+08 0 0.5196 0.5196 chr19 44060511 44061036 0 0.5128 0.5128 chr61.08E+08 1.08E+08 0 0.5 0.5 chr3 71261611 71262501 0 0.4587 0.4587 chr936247847 36248885 0 0.447 0.447 chr19 17819240 17820082 0 0.4279 0.4279chr17 53769900 53770731 0 0.4102 0.4102 chr1 1.12E+08 1.12E+08 0.00020.6167 0.6166 chr7 1.34E+08 1.34E+08 0.0003 0.4351 0.4348 chr3 1165855011659929 0.0004 0.4299 0.4295 chr17 53764417 53765963 0.0005 0.79670.7961 chr10 11246762 11249052 0.0005 0.4002 0.3997 chr22 2899264728993434 0.0006 0.8092 0.8087 chr15 62460278 62461007 0.0006 0.43340.4328 chr1 31002038 31003474 0.0007 0.5926 0.5919 chr19  3129246 3132159 0.0008 0.7725 0.7717 chr12 1.21E+08 1.21E+08 0.0008 0.73030.7295 chr19 12304446 12305741 0.0009 0.6986 0.6978 chr3 6778873467789395 0.001 0.9131 0.9121 chr9 1.32E+08 1.32E+08 0.001 0.7047 0.7037chr19  6723370  6724479 0.001 0.689 0.688 chr3 1.84E+08 1.84E+08 0.0010.4384 0.4374 chr2 53848089 53849214 0.001 0.4368 0.4358 chr17 5945088659452113 0.0012 0.469 0.4678 chr5 1.72E+08 1.72E+08 0.0014 0.578 0.5766chr21 35342527 35343373 0.0014 0.5392 0.5378 chr21 45164804 451654370.0015 0.4251 0.4236 chrX  3742417  3744601 0.0016 0.4486 0.447 chr2145158293 45159003 0.0017 0.7799 0.7782 chr7 39839340 39839876 0.00170.4074 0.4057 chr2 1.75E+08 1.75E+08 0.0018 0.4816 0.4797 chr12 1.24E+081.24E+08 0.0019 0.6306 0.6287 chr3 50352688 50353823 0.002 0.624 0.622chr9 97264382 97265523 0.0021 0.5008 0.4987 chr7 64178628 641793540.0021 0.4088 0.4066 chr9 94767202 94767802 0.0023 0.7568 0.7544 chr542986308 42988304 0.0023 0.4882 0.4859 chr17 63854127 63854693 0.00240.8266 0.8242 chr12 1.22E+08 1.22E+08 0.0024 0.4869 0.4844 chr1716260170 16260909 0.0026 0.6404 0.6378 chr4 39874787 39875456 0.00270.7233 0.7206 chr12  6441080  6441608 0.0027 0.6228 0.6201 chr1945015653 45016886 0.0028 0.5444 0.5416 chr6 30757752 30758823 0.00280.4783 0.4755 chr6 41636176 41637112 0.0028 0.4254 0.4226 chr12  6315199 6315765 0.0029 0.4613 0.4584 chr14 76576283 76577070 0.0029 0.43650.4336 chr16 48857790 48858300 0.0031 0.5625 0.5594 chr5  1.7E+08 1.7E+08 0.0031 0.4752 0.4721 chr13 26897813 26898557 0.0032 0.43540.4322 chr14 52753948 52754571 0.0032 0.4221 0.4189 chr1 1.66E+081.66E+08 0.0033 0.5579 0.5545 chr12 56157424 56158348 0.0033 0.47 0.4667chr22 16079971 16080532 0.0034 0.6226 0.6193 chr7  1946410  19469750.0036 0.6826 0.6789 chr11   258799   259749 0.0036 0.5072 0.5037 chr613381944 13382477 0.0037 0.5945 0.5908 chr7 1.27E+08 1.27E+08 0.00370.5096 0.5058 chr13 23743886 23744467 0.0037 0.4534 0.4497 chr2 1.21E+081.21E+08 0.0038 0.7175 0.7137 chr21 25855853 25857105 0.0039 0.46610.4622 chr2 43211724 43212565 0.0039 0.4345 0.4306 chr12 1.08E+081.08E+08 0.0041 0.6024 0.5983 chr15 92928924 92929575 0.0041 0.40740.4033 chr19 10731043 10731636 0.0042 0.5868 0.5826 chr6 1.45E+081.45E+08 0.0043 0.5783 0.574 chr1 52875323 52875907 0.0044 0.4145 0.4101chr14 75058186 75058956 0.0045 0.602 0.5975 chr12 1.21E+08 1.21E+080.0045 0.4821 0.4776 chr17 76873737 76874417 0.0046 0.6012 0.5966 chr22.38E+08 2.38E+08 0.0049 0.7654 0.7604 chr2 1.98E+08 1.98E+08 0.00490.7228 0.7179 chr6 1.47E+08 1.47E+08 0.0049 0.4967 0.4918 chr9 1.36E+081.36E+08 0.0049 0.4584 0.4535 chr1 67545402 67546771 0.005 0.4971 0.4921chr6 1.58E+08 1.58E+08 0.0052 0.6145 0.6093 chr3  1.7E+08  1.7E+080.0052 0.5845 0.5794 chr1 2.34E+08 2.34E+08 0.0053 0.7033 0.6979 chr1080715722 80716751 0.0053 0.6515 0.6462 chr4 48602901 48603736 0.00530.6315 0.6262 chr19 13957965 13958580 0.0053 0.599 0.5937 chr1 9008111490082367 0.0053 0.4574 0.4521 chr2 1.06E+08 1.06E+08 0.0054 0.88580.8804 chr16 29664213 29665369 0.0054 0.8339 0.8285 chr1 1.59E+081.59E+08 0.0054 0.7663 0.7608 chr13 97926489 97927025 0.0054 0.62290.6175 chr1 41604452 41605277 0.0054 0.6011 0.5956 chr9 1.28E+081.28E+08 0.0054 0.5871 0.5818

TABLE S3B List of top 100 loci deduced to be hypomethylated from thefirst trimester maternal plasma bisulfite-sequencing data MaternalMethylation Chromosome Start End blood cells CVS difference chr1235771917 235772426 0.9868 0.549 0.4377 chr1 97357972 97358622 0.98350.4805 0.503  chr1 4490516 4491074 0.9826 0.4793 0.5032 chr4 181124168181124671 0.9825 0.4725 0.5099 chr16 71908694 71909213 0.982  0.55810.4239 chr3 182727915 182728477 0.981  0.3577 0.6233 chr5 115339535115340038 0.9802 0.5455 0.4347 chr3 195855575 195856122 0.9801 0.37930.6008 chr6 155437621 155438161 0.9799 0.5991 0.3808 chr9 2046809320468904 0.9798 0.4271 0.5527 chr10 90702298 90702987 0.9787 0.33240.6463 chr1 170581654 170582162 0.9785 0.4817 0.4968 chr3 108816849108817794 0.9783 0.4793 0.4989 chr20 36912749 36913319 0.9783 0.5 0.4783chr13 72517281 72517839 0.9782 0.4855 0.4927 chr12 103553001 1035536770.9774 0.492 0.4854 chr22 27638905 27639408 0.9766 0.5385 0.4382 chr717290850 17291462 0.9763 0.59 0.3863 chr6 17227866 17228510 0.976 0.4058 0.5703 chr15 56998547 56999107 0.9754 0.3766 0.5988 chr7 7096594570966842 0.9753 0.5893 0.386  chr3 32159338 32160065 0.9752 0.53790.4372 chr16 17043258 17043854 0.9752 0.5521 0.4231 chr16 2277622322776850 0.9752 0.5735 0.4017 chr5 169344029 169344869 0.9751 0.42110.5541 chr11 34324955 34325722 0.975  0.5561 0.4189 chr8 5855474558555376 0.9747 0.5784 0.3964 chr1 153933389 153934121 0.9746 0.4630.5116 chr14 88003983 88004485 0.9745 0.5379 0.4366 chr3 151738501151739120 0.9741 0.4901 0.484  chr14 105618699 105619606 0.974  0.34570.6283 chr16 24060085 24060702 0.9738 0.3991 0.5747 chr8 6894179268942711 0.9738 0.5449 0.429  chr12 53208707 53209304 0.9737 0.48470.489  chr7 76892564 76893249 0.9736 0.5664 0.4072 chr3 6946429469464971 0.9736 0.5893 0.3843 chr19 61401137 61401745 0.9732 0.49330.4799 chr11 124569867 124570490 0.9732 0.5136 0.4595 chr18 4261844042619096 0.9732 0.5942 0.379  chr5 169398896 169399637 0.9731 0.4980.4751 chr5 169328124 169328983 0.9731 0.572 0.401  chr20 3467988034680448 0.9731 0.5922 0.3809 chr16 9042198 9042702 0.973  0.4286 0.5444chr10 90205044 90205701 0.973  0.4407 0.5323 chr13 33236454 332369970.973  0.5906 0.3824 chr16 73284579 73285087 0.9729 0.5602 0.4127 chr829100691 29101428 0.9728 0.505 0.4678 chr2 202383851 202384447 0.97270.5461 0.4267 chr3 179501620 179502300 0.9722 0.5766 0.3956 chr6107674976 107675906 0.9719 0.4434 0.5285 chr6 107880632 107881161 0.97180.5623 0.4095 chr12 56350283 56350933 0.9718 0.5909 0.3809 chr1940636458 40637339 0.9717 0.4941 0.4776 chr2 223472599 223473287 0.97140.1824 0.7891 chr22 20709067 20709787 0.9714 0.5149 0.4565 chr1946095583 46096190 0.9713 0.5385 0.4328 chr6 90258338 90259318 0.97120.3415 0.6297 chr2 54598347 54598933 0.9712 0.5894 0.3819 chr3 114810453114811493 0.9711 0.5166 0.4545 chr19 15851125 15851654 0.9711 0.52360.4476 chr8 42889138 42890084 0.9711 0.5652 0.4059 chr18 5235439052355064 0.971  0.598 0.373  chr15 38206236 38207010 0.9709 0.41860.5523 chr7 99700554 99701110 0.9708 0.305 0.6658 chr12 1948733619487855 0.9708 0.4105 0.5603 chr7 87996908 87997437 0.9708 0.54620.4246 chr6 63628653 63629378 0.9707 0.529 0.4417 chr15 3820910838209618 0.9706 0.5882 0.3824 chr19 6623769 6624450 0.9704 0.5179 0.4526chr2 10794513 10795242 0.9704 0.5976 0.3728 chr2 118472785 1184744540.9704 0.5992 0.3712 chr5 57820209 57820801 0.9701 0.5815 0.3886 chr10100183380 100184702 0.9701 0.5826 0.3875 chr2 8151989 8152646 0.97  0.4701 0.4999 chr10 3938374 3938914 0.9699 0.1741 0.7958 chr9 123724524123725439 0.9697 0.57 0.3997 chr14 89085469 89086097 0.9696 0.32780.6418 chr16 14129437 14130133 0.9695 0.5304 0.4392 chr5 6074636760747191 0.9695 0.5571 0.4124 chr1 92002953 92003729 0.9694 0.52 0.4494chr6 31264677 31265413 0.9693 0.5135 0.4558 chr7 99317013 993182810.9692 0.5117 0.4574 chr8 8808867 8809422 0.9692 0.5691 0.4002 chr1920052165 20052720 0.969  0.2792 0.6898 chr8 129139026 129139573 0.969 0.3458 0.6232 chr11 122314929 122315458 0.969  0.4232 0.5458 chr1398377663 98378165 0.9688 0.3319 0.6369 chr9 107606194 107606872 0.96880.449 0.5198 chr8 56096904 56097736 0.9688 0.5267 0.4422 chr7 128093836128094339 0.9688 0.5929 0.3758 chr2 103109370 103109916 0.9686 0.33330.6352 chr3 101803534 101804063 0.9686 0.5027 0.4659 chr10 6950572069506278 0.9684 0.2515 0.7169 chr13 26608225 26608754 0.9683 0.36140.6069 chr1 90993315 90993828 0.9683 0.5519 0.4164 chr6 1136124311361801 0.9681 0.2578 0.7103 chr21 36529300 36529981 0.968  0.19440.7736 chr21 37813953 37814521 0.9679 0.2175 0.7505 chr2 1522627315227211 0.9679 0.5134 0.4545 chr19 4102809 4103443 0.9679 0.5646 0.4034

TABLE S3C List of top 100 loci deduced to be hypermethylated from thethird trimester maternal plasma bisulfite-sequencing data Maternal bloodTerm Methylation Chromosome Start End cells placenta difference chr1753763065 53764027 0.0000 0.8680 0.8680 chr22 39189067 39189863 0.00000.8233 0.8233 chr10 30858286 30858871 0.0000 0.7713 0.7713 chr7 4188769441888212 0.0000 0.7578 0.7578 chr2 1.14E+08 1.14E+08 0.0000 0.75000.7500 chr12 25096242 25097206 0.0000 0.7332 0.7332 chr6 1.07E+081.07E+08 0.0000 0.7229 0.7229 chr1 66574104 66574793 0.0000 0.71360.7136 chr16 11298755 11299326 0.0000 0.7005 0.7005 chr6 1148998511490755 0.0000 0.6935 0.6935 chr18 13454740 13455292 0.0000 0.65940.6594 chr6 1.08E+08 1.08E+08 0.0000 0.6231 0.6231 chrX  3627885 3628549 0.0000 0.6133 0.6133 chr12  7979754  7980413 0.0000 0.61180.6118 chr3 71261611 71262501 0.0000 0.5938 0.5938 chr17 5376990053770731 0.0000 0.5586 0.5586 chr11 1.18E+08 1.18E+08 0.0000 0.55580.5558 chr19 44060511 44061036 0.0000 0.5464 0.5464 chr2 2.38E+082.38E+08 0.0000 0.5330 0.5330 chr1 1.91E+08 1.91E+08 0.0000 0.52940.5294 chr1 1.44E+08 1.44E+08 0.0000 0.4857 0.4857 chr2 1.75E+081.75E+08 0.0000 0.4785 0.4785 chr4 15366889 15367646 0.0000 0.47290.4729 chr2 19537237 19537737 0.0000 0.4599 0.4599 chr1 1.15E+081.15E+08 0.0000 0.4351 0.4351 chr1 1.54E+08 1.54E+08 0.0000 0.42990.4299 chr14 51383387 51384149 0.0000 0.4186 0.4186 chr1 1.12E+081.12E+08 0.0002 0.5350 0.5348 chr3 11658550 11659929 0.0004 0.55790.5575 chr17 53764417 53765963 0.0005 0.7894 0.7889 chr22 2899264728993434 0.0006 0.8053 0.8047 chr6 27214981 27215823 0.0006 0.45930.4587 chr1 31002038 31003474 0.0007 0.6309 0.6302 chr12 1.21E+081.21E+08 0.0008 0.7360 0.7352 chr19  3129246  3132159 0.0008 0.72570.7249 chr19 12304446 12305741 0.0009 0.6397 0.6388 chr6 2872391828724965 0.0009 0.4344 0.4335 chr19  6723370  6724479 0.0010 0.72800.7270 chr2 53848089 53849214 0.0010 0.4060 0.4050 chr9 1.32E+081.32E+08 0.0010 0.7558 0.7548 chr3 67788734 67789395 0.0010 0.92190.9209 chr19 18276368 18277132 0.0011 0.5136 0.5125 chr17 5945088659452113 0.0012 0.5196 0.5184 chr17 74243852 74244670 0.0012 0.41170.4105 chr3 1.85E+08 1.85E+08 0.0014 0.4961 0.4948 chr21 3534252735343373 0.0014 0.5126 0.5112 chr5 1.72E+08 1.72E+08 0.0014 0.65310.6516 chr21 45164804 45165437 0.0015 0.4364 0.4349 chrX  3742417 3744601 0.0016 0.7517 0.7501 chr21 45158293 45159003 0.0017 0.81800.8163 chr2 1.75E+08 1.75E+08 0.0018 0.6214 0.6196 chr12 1.24E+081.24E+08 0.0019 0.5906 0.5887 chr3 50352688 50353823 0.0020 0.60820.6062 chr9 94767202 94767802 0.0023 0.8327 0.8304 chr17 6385412763854693 0.0024 0.7886 0.7862 chr12 1.22E+08 1.22E+08 0.0024 0.50210.4997 chr17 16260170 16260909 0.0026 0.5780 0.5754 chr12  6441080 6441608 0.0027 0.7471 0.7444 chr4 39874787 39875456 0.0027 0.79620.7935 chr18 50536032 50536649 0.0027 0.4920 0.4893 chr6 3075775230758823 0.0028 0.4029 0.4001 chr19  2571763  2572292 0.0031 0.42000.4169 chr5  1.7E+08  1.7E+08 0.0031 0.4218 0.4187 chr13 2689781326898557 0.0032 0.6485 0.6453 chr12 56157424 56158348 0.0033 0.55410.5508 chr1 1.66E+08 1.66E+08 0.0033 0.5147 0.5113 chr22 1607997116080532 0.0034 0.6265 0.6231 chr6 16820551 16821134 0.0035 0.48000.4765 chr11   258799   259749 0.0036 0.5475 0.5439 chr7  1946410 1946975 0.0036 0.8251 0.8215 chr6 13381944 13382477 0.0037 0.82210.8183 chr7 1.27E+08 1.27E+08 0.0037 0.4767 0.4730 chr2 1.21E+081.21E+08 0.0038 0.6734 0.6697 chr2 43211724 43212565 0.0039 0.42560.4217 chr15 92928924 92929575 0.0041 0.5605 0.5564 chr12 1.08E+081.08E+08 0.0041 0.7313 0.7271 chr19 10731043 10731636 0.0042 0.56680.5626 chr6 1.45E+08 1.45E+08 0.0043 0.5910 0.5867 chr1 5287532352875907 0.0044 0.6115 0.6071 chr12 1.21E+08 1.21E+08 0.0045 0.58840.5839 chr14 75058186 75058956 0.0045 0.6534 0.6489 chr17 7687373776874417 0.0046 0.5658 0.5612 chr6 1.47E+08 1.47E+08 0.0049 0.58260.5777 chr2 1.98E+08 1.98E+08 0.0049 0.7944 0.7895 chr2 2.38E+082.38E+08 0.0049 0.7328 0.7278 chr8 1.42E+08 1.42E+08 0.0050 0.77280.7679 chr3  1.7E+08  1.7E+08 0.0052 0.7227 0.7176 chr6 1.58E+081.58E+08 0.0052 0.6389 0.6337 chr2 2.38E+08 2.38E+08 0.0052 0.42380.4185 chr11 56948854 56949496 0.0053 0.4484 0.4431 chr4 4860290148603736 0.0053 0.5920 0.5867 chr5 1.31E+08 1.31E+08 0.0053 0.48580.4805 chr10 80715722 80716751 0.0053 0.5249 0.5196 chr19 1395796513958580 0.0053 0.4379 0.4326 chr1 2.34E+08 2.34E+08 0.0053 0.84400.8387 chr13 97926489 97927025 0.0054 0.7233 0.7179 chr9 1.28E+081.28E+08 0.0054 0.7312 0.7258 chr2 1.06E+08 1.06E+08 0.0054 0.85130.8459 chr2 96556705 96557637 0.0054 0.4095 0.4041 chr16 2966421329665369 0.0054 0.8837 0.8783

TABLE S3D List of top 100 loci deduced to be hypomethylated from theMaternal blood Term Methylation Chromosome Start End cells placentadifference chr10  7548948  7549483 0.9866 0.5685 0.4181 chr1  4490516 4491074 0.9826 0.5015 0.4810 chr4 1.81E+08 1.81E+08 0.9825 0.59810.3843 chr3 1.83E+08 1.83E+08 0.9810 0.2925 0.6886 chr3 1.96E+081.96E+08 0.9801 0.4643 0.5158 chr6 1.55E+08 1.55E+08 0.9799 0.46100.5189 chr1 1.71E+08 1.71E+08 0.9785 0.5122 0.4662 chr20 3691274936913319 0.9783 0.4513 0.5269 chr22 38583100 38583616 0.9783 0.54280.4355 chr1  19391314 19392207 0.9778 0.5273 0.4505 chr5 1.74E+081.74E+08 0.9770 0.5852 0.3918 chr19 13678906 13679531 0.9760 0.58120.3949 chr14 83650790 83651395 0.9760 0.5378 0.4382 chr15 5699854756999107 0.9754 0.4691 0.5063 chr16 22776223 22776850 0.9752 0.51140.4638 chr5 1.69E+08 1.69E+08 0.9751 0.4809 0.4943 chr8 5855474558555376 0.9747 0.5977 0.3770 chr14 1.06E+08 1.06E+08 0.9740 0.20690.7671 chr8 68941792 68942711 0.9738 0.5872 0.3866 chr16 2406008524060702 0.9738 0.3470 0.6268 chr12 53208707 53209304 0.9737 0.52780.4459 chr5 1.69E+08 1.69E+08 0.9731 0.5057 0.4673 chr16  9042198 9042702 0.9730 0.1860 0.7869 chr10 90205044 90205701 0.9730 0.59220.3808 chr3 1.89E+08 1.89E+08 0.9720 0.4949 0.4771 chr6 1.08E+081.08E+08 0.9719 0.5825 0.3894 chr2 2.23E+08 2.23E+08 0.9714 0.33330.6381 chr19 46095583 46096190 0.9713 0.5065 0.4648 chr8 1.41E+081.41E+08 0.9713 0.5753 0.3959 chr6 90258338 90259318 0.9712 0.43570.5355 chr13 51403556 51404069 0.9710 0.3980 0.5731 chr18 6687504866875726 0.9710 0.5259 0.4451 chr7 99700554 99701110 0.9708 0.37570.5951 chr7 87996908 87997437 0.9708 0.5720 0.3988 chr19  6623769 6624450 0.9704 0.4774 0.4930 chr1 97639047 97639749 0.9701 0.41480.5553 chr16 23892096 23892772 0.9701 0.5000 0.4701 chr10  3938374 3938914 0.9699 0.1148 0.8551 chr14 89085469 89086097 0.9696 0.29640.6732 chr8 1.29E+08 1.29E+08 0.9690 0.3565 0.6126 chr13 9837766398378165 0.9688 0.3123 0.6566 chr8 56096904 56097736 0.9688 0.45620.5126 chr2 1.03E+08 1.03E+08 0.9686 0.3459 0.6227 chr13 2660822526608754 0.9683 0.4562 0.5121 chr2 22738157 22738760 0.9682 0.51220.4560 chr6 11361243 11361801 0.9681 0.2646 0.7035 chr21 3652930036529981 0.9680 0.1829 0.7852 chr21 37813953 37814521 0.9679 0.30610.6619 chr2 2.43E+08 2.43E+08 0.9679 0.5750 0.3929 chr4 1241354312414103 0.9679 0.5944 0.3735 chr3 1.27E+08 1.27E+08 0.9677 0.40300.5648 chr7 33509047 33509556 0.9676 0.4627 0.5048 chr14 5928484659285553 0.9674 0.5254 0.4420 chr17 42623453 42624024 0.9673 0.43180.5355 chr19  6778363  6779377 0.9671 0.4416 0.5255 chr4 4179825041798788 0.9670 0.5000 0.4670 chr5 88054080 88054588 0.9669 0.22380.7431 chr16 24109379 24110289 0.9669 0.5062 0.4607 chr10 1384715913847895 0.9667 0.3188 0.6479 chr10 1.27E+08 1.27E+08 0.9667 0.54230.4244 chr12 1.12E+08 1.12E+08 0.9663 0.3722 0.5941 chr10 1722088617221845 0.9662 0.4455 0.5207 chr8  5947355  5947862 0.9662 0.51710.4491 chr3 73740840 73741439 0.9659 0.3657 0.6002 chr14 5794595357946875 0.9658 0.5357 0.4301 chr14 50905777 50906333 0.9658 0.30080.6650 chr15 90275374 90276000 0.9657 0.5409 0.4248 chr22 2471729924718197 0.9657 0.5160 0.4497 chr7 36530128 36530987 0.9656 0.51940.4462 chr2 1.31E+08 1.31E+08 0.9655 0.4384 0.5271 chr4 4211698842117788 0.9654 0.5195 0.4459 chr12 1.16E+08 1.16E+08 0.9653 0.55940.4059 chr2  7491785  7492736 0.9652 0.4556 0.5097 chr19  6599638 6600187 0.9652 0.5488 0.4163 chr6 25326803 25327398 0.9651 0.39740.5677 chr4  1.7E+08  1.7E+08 0.9651 0.4933 0.4718 chr7 9987533899876155 0.9650 0.2696 0.6953 chr14 97144328 97145208 0.9649 0.53770.4272 chr3 11718596 11719163 0.9649 0.5521 0.4128 chr14   1E+08   1E+080.9649 0.3794 0.5855 chr7  1.5E+08  1.5E+08 0.9648 0.3327 0.6322 chr1256357827 56358328 0.9648 0.4217 0.5430 chr10  8275750  8276276 0.96470.3100 0.6547 chr11 16999685 17000209 0.9647 0.2765 0.6882 chr2234419356 34419861 0.9646 0.4245 0.5401 chr18 72453151 72453725 0.96460.4700 0.4946 chr5 49919879 49920699 0.9645 0.3169 0.6476 chr1 2458089124581805 0.9643 0.3565 0.6078 chr22 18233774 18234492 0.9641 0.52050.4436 chr14 45356178 45356903 0.9640 0.3934 0.5706 chr3 5300719353008661 0.9638 0.4902 0.4737 chr4 55027912 55028539 0.9637 0.52540.4384 chr5 1.37E+08 1.37E+08 0.9637 0.5290 0.4347 chr1 2.23E+082.23E+08 0.9636 0.0997 0.8640 chr7 1.35E+08 1.35E+08 0.9636 0.29590.6677 chr5 80350438 80351169 0.9636 0.4969 0.4667 chr12 3188960031890343 0.9636 0.1745 0.7891 chr12  8365395  8366096 0.9636 0.57210.3914 chr19 15424819 15425355 0.9635 0.2836 0.6799 chr10 1098546910986409 0.9635 0.4877 0.4759

What is claimed is:
 1. A method of detecting a chromosomal abnormalityfrom a biological sample of a subject, the biological sample includingcell-free DNA comprising a mixture of cell-free DNA originating from afirst tissue and from a second tissue, the method comprising: analyzinga plurality of cell-free DNA molecules from the biological sample,wherein analyzing a cell-free DNA molecule includes: determining alocation of the cell-free DNA molecule in a reference genome;determining whether the cell-free DNA molecule is methylated at one ormore sites, the one or more sites of each of the plurality of cell-freeDNA molecules providing a plurality of sites; for each of the pluralityof sites: determining a respective number of cell-free DNA moleculesthat are methylated at the site; calculating a first methylation levelof a first chromosomal region based on the respective numbers ofcell-free DNA molecules methylated at sites within the first chromosomalregion; comparing the first methylation level to a cutoff value; anddetermining a classification of an abnormality in the first tissue forthe first chromosomal region based on the comparison.
 2. The method ofclaim 1, wherein comparing the first methylation level to the cutoffvalue includes: normalizing the first methylation level and comparingthe normalized first methylation level to the cutoff value.
 3. Themethod of claim 2, wherein the normalization uses a second methylationlevel of a second chromosomal region.
 4. The method of claim 2, whereinthe normalization uses a fractional concentration of cell-free DNA fromthe first tissue.
 5. The method of claim 2, wherein the normalizationuses a total number of the cell-free DNA molecules that are analyzed. 6.The method of claim 1, further comprising: identifying a plurality ofadditional chromosomal regions of a genome of the subject; identifying aplurality of additional sites within each of the additional chromosomalregions; calculating a region methylation level for each additionalchromosomal region; comparing each of the region methylation levels to arespective region cutoff value; determining a first number of regionswhose region methylation level exceeds the respective region cutoffvalue; and comparing the first number to a threshold value to determinea classification of a level of the abnormality in the subject.
 7. Themethod of claim 6, wherein the abnormality is cancer.
 8. The method ofclaim 6, wherein the threshold value is a percentage, and whereincomparing the first number to the threshold value includes: dividing thefirst number of regions by a second number of regions before comparingto the threshold value.
 9. The method of claim 8, wherein the secondnumber of regions is all of the identified plurality of regions.
 10. Themethod of claim 6, wherein the respective region cutoff values are aspecified amount from a reference methylation level.
 11. The method ofclaim 1, wherein the first tissue is from a tumor within the subject andthe second tissue is from a non-malignant tissue of the subject.
 12. Themethod of claim 11, further comprising: determining a fractionalconcentration of tumor DNA in the biological sample; and calculating thecutoff value based on the fractional concentration of tumor DNA in thebiological sample.
 13. The method of claim 1, wherein the cutoff valueis determined from other subjects not having cancer.
 14. The method ofclaim 1, wherein the cutoff value is dependent on a concentration ofcell-free DNA originating from the first tissue in the biologicalsample.
 15. The method of claim 14, wherein the cutoff value is based ona scale factor corresponding to a type of abnormality, where the type ofabnormality is deletion or duplication.
 16. The method of claim 1,wherein determining the location of the cell-free DNA molecule in thereference genome involves determining whether the location is within thefirst chromosomal region.
 17. The method of claim 16, whereindetermining the location of the cell-free DNA molecule in the referencegenome is accomplished by determining whether the cell-free DNA moleculemaps to the first chromosomal region.
 18. The method of claim 1, whereinthe chromosomal abnormality is a subchromosomal deletion orsubchromosomal amplification.
 19. The method of claim 1, whereindetermining whether the cell-free DNA molecule is methylated at the oneor more sites comprises performing methylation-aware sequencing.
 20. Themethod of claim 19, wherein performing the methylation-aware sequencingcomprises performing methylation-aware massively parallel sequencing.21. The method of claim 19, wherein performing methylation-awaresequencing generates at least 10 million reads.
 22. The method of claim19, wherein performing methylation-aware sequencing includes: treatingthe cell-free DNA molecules with sodium bisulfite; and performingsequencing of the treated cell-free DNA molecules.
 23. The method ofclaim 22, wherein treating the cell-free DNA molecules with the sodiumbisulfite is part of Tet-assisted bisulfite conversion or oxidativebisulfite sequencing for a detection of 5-hydroxymethylcytosine.
 24. Themethod of claim 19, wherein performing methylation-aware sequencingcomprises sequencing of at least 60,000 cell-free DNA molecules.
 25. Themethod of claim 1, wherein the plurality of sites are CpG sites.
 26. Themethod of claim 1, wherein the cutoff value is a specified distance froma reference methylation level established from another biological sampleobtained from a healthy subject or a chromosomal region that does nothave the abnormality.
 27. The method of claim 26, wherein the specifieddistance is a specified number of standard deviations from the referencemethylation level.
 28. The method of claim 1, wherein the cutoff valueis established from a reference methylation level determined from aprevious biological sample of the subject obtained previous to thebiological sample being tested.
 29. The method of claim 1, whereincomparing the first methylation level to the cutoff value includes:determining a difference between the first methylation level and areference methylation level; and comparing the difference to a thresholdcorresponding to the cutoff value.
 30. The method of claim 1, furthercomprising: measuring a size of the cell-free DNA molecules at theplurality of sites, thereby obtaining measured sizes; and beforecomparing the first methylation level to the first cutoff value,normalizing the first methylation level using cell-free DNA moleculeshaving a first size.
 31. The method of claim 30, wherein normalizing thefirst methylation level using the cell-free DNA molecules having thefirst size includes: obtaining a functional relationship between sizeand methylation levels; and using the functional relationship tonormalize the first methylation level, wherein the functionalrelationship provides scaling values corresponding to respective sizes.32. The method of claim 1, wherein analyzing the plurality of cell-freeDNA molecules comprises analyzing at least 10 million sequence reads.33. A computer product comprising a non-transitory computer readablemedium storing a plurality of instructions that when executed control acomputer system to perform: analyzing a plurality of cell-free DNAmolecules from a biological sample, the biological sample includingcell-free DNA comprising a mixture of cell-free DNA originating from afirst tissue and from a second tissue, wherein analyzing a cell-free DNAmolecule includes: determining a location of the cell-free DNA moleculein a reference genome; determining whether the cell-free DNA molecule ismethylated at one or more sites, the one or more sites of each of theplurality of cell-free DNA molecules providing a plurality of sites; foreach of the plurality of sites: determining a respective number ofcell-free DNA molecules that are methylated at the site; calculating afirst methylation level of a first chromosomal region based on therespective numbers of cell-free DNA molecules methylated at sites withinthe first chromosomal region; comparing the first methylation level to acutoff value; and determining a classification of an abnormality in thefirst tissue for the first chromosomal region based on the comparison.